Processing device with intuitive learning capability

• US 20030149675A1
• Filed: 08/30/2002
• Published: 08/07/2003
• Est. Priority Date: 06/26/2001
• Status: Active Grant

• Current Assignee: Intuition Intelligence Incorporated
• Original Assignee: Intuitive Intelligence, Inc.
• Inventors: Ansari, Arif M., Shiek Ansari, Yusuf Sulaiman M.
• Granted Patent: US 7,483,867 B2
• Time in Patent Office: Days
• US Class Current: 706/2
• CPC Class Codes: A63F 13/10 Control of the course of the game, e.g. start, progess, end
• CPC Class Codes: A63F 13/12 involving interaction between a plurality of game devices, e.g. transmisison or distribution systems protocols for networked virtual reality, networked games H04L29/06034
• CPC Class Codes: A63F 13/23 for interfacing with the game device, e.g. specific interfaces between game controller and console
• CPC Class Codes: A63F 13/30 Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
• CPC Class Codes: A63F 13/338 using television networks
• CPC Class Codes: A63F 13/45 Controlling the progress of the video game
• CPC Class Codes: A63F 13/67 adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
• CPC Class Codes: A63F 13/798 for assessing skills or for ranking players, e.g. for generating a hall of fame computing the game score A63F13/46
• CPC Class Codes: A63F 2300/558 by assessing the players' skills or ranking
• CPC Class Codes: A63F 2300/6027 using adaptive systems learning from user actions, e.g. for skill level adjustment
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/443 OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4751 for defining user accounts, e.g. accounts for children

Abstract

A method and apparatus for providing learning capability to processing device, such as a computer game, educational toy, telephone, or television remote control, is provided to achieve one or more objective. For example, if the processing device is a computer game, the objective may be to match the skill level of the game with that of a player. If the processing device is an educational toy, the objective may be to increase the educational level of a user. If the processing device is a telephone, the objective may be to anticipate the phone numbers that a phone user will call. If the processing device is a television remote control, the objective may be to anticipate the television channels that will watched by the user. One of a plurality of actions (e.g., game actions, educational prompts, listed phone numbers, or listed television channels) to be performed on the processing device is selected. A user input indicative of a user action (e.g., a player action, educational input, called phone number, or watched television channel) is received. An outcome of the selected action and/or user action is determined. For example, in the case of a computer game, the outcome may indicate whether a computer-manipulated object has intersected a user-manipulated object. In the case of an educational toy, the outcome may indicate whether a user action matches a prompt generated by the educational toy. In the case of a telephone, the outcome may indicate whether a called phone number is on a list of phone numbers. In the case of a television remote control, the outcome may indicate whether a watched television channel is on a list of television channels. An action probability distribution that includes probability values corresponding to the plurality of actions is then updated based on the determined outcome. The next action will then be selected based on this updated action probability distribution. The foregoing steps can be modified based on a performance index to achieve the objective of the processing device so that it learns.

Claims

47. A processing device having one or more objectives, comprising:

a probabilistic learning module having a learning automaton configured for learning a plurality of processor actions in response to a plurality of actions performed by a user; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said one or more objectives.

48. The processing device of claim 47, wherein said intuition module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said one or more objectives, and for modifying said probabilistic learning module functionality based on said performance index.

49. The processing device of claim 47, wherein said intuition module is deterministic.

50. The processing device of claim 47, wherein said intuition module is quasi-deterministic.

51. The processing device of claim 47, wherein said intuition module is probabilistic.

52. The processing device of claim 47, wherein said intuition module comprises artificial intelligence.

53. The processing device of claim 47, wherein said intuition module comprises an expert system.

54. The processing device of claim 47, wherein said intuition module comprises a neural network.

55. The processing device of claim 47, wherein said intuition module comprises fuzzy logic.

56. The processing device of claim 47, wherein said probabilistic learning module comprises:

an action selection module configured for selecting one of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

an outcome evaluation module configured for determining an outcome of one or both of said identified user action and said selected processor action; and

a probability update module configured for updating said action probability distribution based on said outcome.

57. The processing device of claim 56, wherein said outcome is of said identified user action.

58. The processing device of claim 56, wherein said outcome is of said selected processor action.

59. The processing device of claim 56, wherein said outcome is of said selected processor action relative to said identified user action.

60. The processing device of claim 56, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

61. The processing device of claim 56, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

62. The processing device of claim 56, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

63. The processing device of claim 47, wherein said intuition module is configured for selecting one of a predetermined plurality of algorithms employed by said learning module.

64. The processing device of claim 47, wherein said intuition module is configured for modifying a parameter of an algorithm employed by said learning module.

65. A method of providing learning capability to a computer game having an objective of matching a skill level of said computer game with a skill level of a game player, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining an outcome of said selected game move relative to said identified player move;

updating said game move probability distribution based on said outcome; and

modifying one or more of said game move selection, said outcome determination, and said game move probability distribution update based on said objective.

66. The method of claim 65, wherein said selected game move is selected in response to said identified player move.

67. The method of claim 65, further comprising determining a performance index indicative of a performance of said computer game relative to said objective, wherein said modification is based on said performance index.

68. The method of claim 67, wherein said performance index comprises a relative score value between said game player and said computer game.

69. The method of claim 65, wherein said performance index is updated when an outcome is determined.

70. The method of claim 67, wherein said performance index is derived from an outcome determination.

71. The method of claim 67, wherein said performance index is derived indirectly from an outcome determination.

72. The method of claim 67, wherein said performance index is a function of said game move probability distribution.

73. The method of claim 67, wherein said performance index is cumulative.

74. The method of claim 67, wherein said performance index is instantaneous.

75. The method of claim 65, wherein said modification comprises modifying said game move selection.

76. The method of claim 75, wherein said plurality of game moves are organized into a plurality of game move subsets, said selected game move is selected from one of said plurality of game move subsets, and said subsequent game move selection comprises selecting another of said plurality of game move subsets.

77. The method of claim 76, wherein said game move selection comprises selecting another game move from said another of said plurality of game move subsets in response to another player move.

78. The method of claim 65, wherein said modification comprises modifying said outcome determination.

79. The method of claim 65, wherein said modification comprises modifying said game move probability distribution update.

80. The method of claim 65, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said performed game move selection, said outcome determination, and said game move probability distribution update.

81. The method of claim 65, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said game move selection, said outcome determination, and said game move probability distribution update.

82. The method of claim 65, wherein said outcome can be represented by one of two possible values.

83. The method of claim 82, wherein said two possible values are the integers “

zero” and

“

one.”

84. The method of claim 65, wherein said outcome can be represented by one of a finite range of real numbers.

85. The method of claim 65, wherein said outcome can be represented by one of a range of continuous values.

86. The method of claim 65, wherein said selected game move is a currently selected game move.

87. The method of claim 65, wherein said selected game move is a previously selected game move.

88. The method of claim 65, wherein said selected game move is a subsequently selected game move.

89. The method of claim 65, wherein said outcome is determined by performing a collision technique on said identified player move and said selected game move.

90. The method of claim 65, further comprising initially generating said game move probability distribution with equal probability values.

91. The method of claim 65, further comprising initially generating said game move probability distribution with unequal probability values.

92. The method of claim 65, wherein said game move probability distribution update comprises a linear update.

93. The method of claim 65, wherein said game move probability distribution update comprises a nonlinear update.

94. The method of claim 65, wherein said game move probability distribution update comprises an absolutely expedient update.

95. The method of claim 65, wherein said game move probability distribution update comprises a reward-penalty update.

96. The method of claim 65, wherein said game move probability distribution update comprises a reward-inaction update.

97. The method of claim 65, wherein said game move probability distribution update comprises a inaction-penalty update.

98. The method of claim 65, wherein said game move probability distribution is normalized.

99. The method of claim 65, wherein said selected game move corresponds to the highest probability value within said game move probability distribution.

100. The method of claim 65, wherein said selected game move corresponds to a pseudo- random selection of a probability value within said game move probability distribution.

101. The method of claim 65, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

102. The method of claim 101, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

103. The method of claim 101, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

104. The method of claim 101, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

105. The method of claim 101, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

106. The method of claim 65, wherein said game move probability distribution is updated using a learning automaton.

107. A computer game having an objective of matching a skill level of said computer game with a skill level of a game player, comprising:

a probabilistic learning module configured for learning a plurality of game moves in response to a plurality of moves performed by a game player; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said objective.

108. The computer game of claim 107, wherein said intuition module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and for modifying said probabilistic learning module functionality based on said performance index.

109. The computer game of claim 108, wherein said performance index comprises a relative score value between said game player and said computer game.

110. The computer game of claim 107, wherein said probabilistic learning module comprises:

a game move selection module configured for selecting one of a plurality of game moves, said game move selection being based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

an outcome evaluation module configured for determining an outcome of said selected game move relative to said identified player move; and

a probability update module configured for updating said game move probability distribution based on said outcome.

111. The computer game of claim 110, wherein said intuition module is configured for modifying a functionality of said game move selection module based on said objective.

114. The computer game of claim 110, wherein said intuition module is configured for selecting one of a predetermined plurality of algorithms employed by said learning module.

121. The computer game of claim 107, wherein said probabilistic learning module comprises a learning automaton.

122. A method of providing learning capability to a processing device, comprising:

generating an action probability distribution comprising a plurality of probability values organized among a plurality of action subsets, said plurality of probability values corresponding to a plurality of processor actions;

selecting one of said plurality of action subsets; and

selecting one of said plurality of processor actions from said selected action subset.

123. The method of claim 122, further comprising:

identifying an action performed by a user, determining an outcome of said selected processor action relative to said identified user action; and

updating said action probability distribution based on said outcome.

124. The method of claim 123, wherein said selected processor action is selected in response to said identified user action.

125. The method of claim 122, wherein said processing device has one or more objectives, the method further comprising determining a performance index indicative of a performance of said processing device relative to one or more objectives of said processing device, wherein said action subset selection is based on said performance index.

126. The method of claim 122, wherein said selected action subset is selected deterministically.

127. The method of claim 122, wherein said selected action subset is selected quasi-deterministically.

128. The method of claim 122, wherein said selected action subset is selected probabilistically.

129. The method of claim 122, wherein said selected processor action is pseudo-randomly selected from said selected action subset.

130. The method of claim 122, wherein said selected action subset corresponds to a series of probability values within said action probability distribution.

131. The method of claim 122, wherein said selected action subset corresponds to the highest probability values within said action probability distribution.

132. The method of claim 122, wherein said selected action subset corresponds to the lowest probability values within said action probability distribution.

133. The method of claim 122, wherein said selected action subset corresponds to the middlemost probability values within said action probability distribution.

134. The method of claim 122, wherein said selected action subset corresponds to probability values having an average relative to a threshold value.

135. The method of claim 134, wherein said threshold value is a median probability value within said action probability distribution.

136. The method of claim 134, wherein said threshold value is dynamically adjusted.

137. The method of claim 134, wherein said selected action subset corresponds to probability values having an average greater than said threshold value.

138. The method of claim 134, wherein said selected action subset corresponds to probability values having an average less than said threshold value.

139. The method of claim 134, wherein said selected action subset corresponds to probability values having an average substantially equal to said threshold value.

140. The method of claim 122, wherein said action probability distribution is updated using a learning automaton.

141. A method of providing learning capability to a computer game, comprising:

generating a game move probability distribution comprising a plurality of probability values organized among a plurality of game move subsets, said plurality of probability values corresponding to a plurality of game moves;

selecting one of said plurality of game move subsets; and

selecting one of said plurality of game moves from said selected game move subset.

142. The method of claim 141, wherein said selected game move is selected in response to said identified player move.

143. The method of claim 141, further comprising:

identifying an move performed by a game player;

determining an outcome of said selected game move relative to said identified player move; and

updating said game move probability distribution based on said outcome.

144. The method of claim 143, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

145. The method of claim 144, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

146. The method of claim 144, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

147. The method of claim 144, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

148. The method of claim 144, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

149. The method of claim 141, wherein said selected game move subset is selected deterministically.

150. The method of claim 141, wherein said selected game move subset is selected quasi-deterministically.

151. The method of claim 141, wherein said selected game move subset is selected probabilistically.

152. The method of claim 141, wherein said selected game move is pseudo-randomly selected from said selected game move subset.

153. The method of claim 141, wherein said selected game move subset corresponds to a series of probability values within said game move probability distribution.

154. The method of claim 141, wherein said selected game move subset corresponds to the highest probability values within said game move probability distribution.

155. The method of claim 141, wherein said selected game move subset corresponds to the lowest probability values within said game move probability distribution.

156. The method of claim 141, wherein said selected game move subset corresponds to the middlemost probability values within said game move probability distribution.

157. The method of claim 141, wherein said selected game move subset corresponds to probability values having an average relative to a threshold level.

158. The method of claim 157, wherein said threshold level is a median probability value within said game move probability distribution.

159. The method of claim 157, wherein said threshold level is dynamically adjusted.

160. The method of claim 157, wherein said selected game move subset corresponds to probability values having an average greater than said threshold level.

161. The method of claim 157, wherein said selected game move subset corresponds to probability values having an average less than said threshold level.

162. The method of claim 157, wherein said selected game move subset corresponds to probability values having an average substantially equal to said threshold level.

163. The method of claim 141, wherein said selected game move subset is selected based on a skill level of a game player relative to a skill level of said computer game.

164. The method of claim 163, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

165. The method of claim 163, wherein said game move subset is selected to correspond to the highest probability values within said game move probability distribution if said relative skill level is greater than a threshold level.

166. The method of claim 163, wherein said game move subset is selected to correspond to the lowest probability values within said game move probability distribution if said relative skill level is less than a threshold level.

167. The method of claim 163, wherein said game move subset is selected to correspond to the middlemost probability values within said game move probability distribution if said relative skill level is within a threshold range.

168. The method of claim 163, wherein said game move subset is selected to correspond to probability values having an average relative to a threshold level.

169. The method of claim 168, wherein said threshold level is a median probability value within said game move probability distribution.

170. The method of claim 168, wherein said threshold level is dynamically adjusted based on said relative skill level.

171. The method of claim 168, wherein said game move subset is selected to correspond to probability values having an average greater than said threshold level if said relative skill level value is greater than a relative skill threshold level.

172. The method of claim 168, wherein said game move subset is selected to correspond to probability values having an average less than said threshold level if said relative skill level value is less than a relative skill threshold level.

173. The method of claim 168, wherein said game move subset is selected to correspond to probability values having an average substantially equal to said threshold level if said relative skill level value is within a relative skill threshold range.

174. The method of claim 141, wherein said game move probability distribution is updated using a learning automaton.

175. A method of providing learning capability to a processing device, comprising:

generating an action probability distribution using one or more learning algorithms, said action probability distribution comprising a plurality of probability values corresponding to a plurality of processor actions;

modifying said one or more learning algorithms; and

updating said action probability distribution using said modified one or more learning algorithms.

176. The method of claim 175, further comprising:

identifying an action performed by a user;

selecting one of said plurality of processor actions; and

determining an outcome of one or both of said identified user action and said selected processor action, wherein said action probability distribution update is based on said outcome.

177. The method of claim 176, wherein said outcome is of said identified user action.

178. The method of claim 176, wherein said outcome is of said selected processor action.

179. The method of claim 176, wherein said outcome is of said selected processor action relative to said identified user action.

180. The method of claim 176, wherein said selected processor action is selected in response to said identified user action.

181. The method of claim 175, wherein said processing device has one or more objectives, the method further comprising determining a performance index indicative of a performance of said processing device relative to said one or more objectives, wherein said algorithm modification is based on said performance index.

182. The method of claim 175, wherein said one or more learning algorithms are modified deterministically.

183. The method of claim 175, wherein said one or more learning algorithms are modified quasi-deterministically.

184. The method of claim 175, wherein said one or more learning algorithms are modified probabilistically.

185. The method of claim 175, wherein said one or more algorithms comprises one or more parameters, and said algorithm modification comprises modifying said one or more parameters.

186. The method of claim 175, wherein said one or more parameters are modified in accordance with a function.

187. The method of claim 185, wherein said one or more parameters comprises a reward parameter.

188. The method of claim 185, wherein said one or more parameters comprises a penalty parameter.

189. The method of claim 185, wherein said one or more parameters comprises one or more of a reward parameter and penalty parameter.

190. The method of claim 189, wherein said one or more of a reward parameter and penalty parameter are increased.

191. The method of claim 189, wherein said one or more of a reward parameter and penalty parameter are decreased.

192. The method of claim 189, wherein said one or more of a reward parameter and penalty parameter are modified to a negative number.

193. The method of claim 185, wherein said one or more parameters comprises a reward parameter and a penalty parameter.

194. The method of claim 193, wherein said reward parameter and said penalty parameter are both increased.

195. The method of claim 193, wherein said reward parameter and said penalty parameter are both decreased.

196. The method of claim 193, wherein said reward parameter and said penalty parameter are modified to a negative number.

197. The method of claim 175, wherein said one or more algorithms is linear.

198. The method of claim 175, wherein said action probability distribution is updated using a learning automaton.

199. A method of providing learning capability to a computer game, comprising:

generating a game move probability distribution using one or more learning algorithms, said game move probability distribution comprising a plurality of probability values corresponding to a plurality of game moves;

modifying said one or more learning algorithms; and

updating said game move probability distribution using said modified one or more learning algorithms.

200. The method of claim 199, further comprising:

identifying an move performed by a game player;

selecting one of said plurality of game moves; and

determining an outcome of said selected game move relative to said identified player move, wherein said action probability distribution update is based on said outcome.

201. The method of claim 200, wherein said selected game move is selected in response to said identified player move.

202. The method of claim 199, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

203. The method of claim 202, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

204. The method of claim 202, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

205. The method of claim 202, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

206. The method of claim 202, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

207. The method of claim 199, wherein said one or more learning algorithms are modified deterministically.

208. The method of claim 199, wherein said one or more learning algorithms are modified quasi-deterministically.

209. The method of claim 199, wherein said one or more learning algorithms are modified probabilistically.

210. The method of claim 199, wherein said one or more algorithms comprises one or more parameters, and said algorithm modification comprises modifying said one or more parameters.

211. The method of claim 210, wherein said one or more parameters are modified in accordance with a function.

212. The method of claim 210, wherein said one or more parameters comprises a reward parameter.

213. The method of claim 210, wherein said one or more parameters comprises a penalty parameter.

214. The method of claim 210, wherein said one or more parameters comprises one or more of a reward parameter and penalty parameter.

215. The method of claim 214, wherein said one or more of a reward parameter and penalty parameter are increased.

216. The method of claim 214, wherein said one or more of a reward parameter and penalty parameter are decreased.

217. The method of claim 214, wherein said one or more of a reward parameter and penalty parameter are modified to a negative number.

218. The method of claim 210, wherein said one or more parameters comprises a reward parameter and a penalty parameter.

219. The method of claim 218, wherein said reward parameter and said penalty parameter are both increased.

220. The method of claim 218, wherein said reward parameter and said penalty parameter are both decreased.

221. The method of claim 218, wherein said reward parameter and said penalty parameter are modified to a negative number.

222. The method of claim 210, wherein said modified one or more algorithms is modified based on a skill level of a game player relative to a skill level of said computer game.

223. The method of claim 210, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

224. The method of claim 210, wherein said one or more algorithms comprises one or more of a reward parameter and a penalty parameter, and said algorithm modification comprises modifying said one or more of a reward parameter and a penalty parameter based on a skill level of game player relative to a skill level of said computer game.

225. The method of claim 224, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

226. The method of claim 224, wherein said one or more of a reward parameter and a penalty parameter is increased if said relative skill level is greater than a threshold level.

227. The method of claim 224, wherein said one or more of a reward parameter and a penalty parameter is decreased if said relative skill level is less than a threshold level.

228. The method of claim 224, wherein said one or more of a reward parameter and a penalty parameter is modified to be a negative number if said relative skill level is less than a threshold level.

229. The method of claim 199, wherein said one or more algorithms is linear.

230. The method of claim 199, wherein said one or more algorithms comprises a reward parameter and a penalty parameter, and said algorithm modification comprises modifying both of said reward parameter and said penalty parameter based on a skill level of game player relative to a skill level of said computer game.

231. The method of claim 230, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

232. The method of claim 230, wherein both of said reward parameter and said penalty parameter are increased if said relative skill level is greater than a threshold level.

233. The method of claim 230, wherein both of said reward parameter and said penalty parameter are decreased if said relative skill level is less than a threshold level.

234. The method of claim 230, wherein both of said reward parameter and said penalty parameter are modified to be a negative number if said relative skill level is less than a threshold level.

235. The method of claim 230, wherein said one or more algorithms is linear.

236. The method of claim 199, wherein said game move probability distribution is updated using a learning automaton.

237. A method of matching a skill level of game player with a skill level of a computer game, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining if said selected game move is successful;

determining a current skill level of said game player relative to a current skill level of said computer game; and

updating said game move probability distribution using a reward algorithm if said selected game move is successful and said relative skill level is relatively high, or if said selected game move is unsuccessful and said relative skill level is relatively low.

238. The method of claim 237, wherein said selected game move is selected in response to said identified player move.

239. The method of claim 237, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

240. The method of claim 237, wherein said relative skill level is determined to be relatively high if greater than a first threshold value, and relatively low if lower than a second threshold value.

241. The method of claim 237, wherein said reward algorithm is linear.

242. The method of claim 237, further comprising modifying said reward algorithm based on said successful game move determination.

243. The method of claim 237, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

244. The method of claim 243, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

245. The method of claim 243, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

246. The method of claim 243, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

247. The method of claim 243, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

248. The method of claim 237, wherein said game move probability distribution is updated using a learning automaton.

249. A method of matching a skill level of game player with a skill level of a computer game, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining if said selected game move is successful;

determining a current skill level of said game player relative to a current skill level of said computer game; and

updating said game move probability distribution using a penalty algorithm if said selected game move is unsuccessful and said relative skill level is relatively high, or if said selected game move is successful and said relative skill level is relatively low.

250. The method of claim 249, wherein said selected game move is selected in response to said identified player move.

251. The method of claim 249, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

252. The method of claim 249, wherein said relative skill level is determined to be relatively high if greater than a first threshold value, and relatively low if lower than a second threshold value.

253. The method of claim 249, wherein said penalty algorithm is linear.

254. The method of claim 249, further comprising modifying said penalty algorithm based on said successful game move determination.

255. The method of claim 249, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

256. The method of claim 255, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

257. The method of claim 255, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

258. The method of claim 255, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

259. The method of claim 255, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

260. The method of claim 249, wherein said game move probability distribution is updated using a learning automaton.

261. A method of matching a skill level of game player with a skill level of a computer game, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining if said selected game move is successful;

determining a current skill level of said game player relative to a current skill level of said computer game;

updating said game move probability distribution using a reward algorithm if said selected game move is successful and said relative skill level is relatively high, or if said selected game move is unsuccessful and said relative skill level is relatively low; and

updating said game move probability distribution using a penalty algorithm if said selected game move is unsuccessful and said relative skill level is relatively high, or if said selected game move is successful and said relative skill level is relatively low.

262. The method of claim 261, wherein said selected game move is selected in response to said identified player move.

263. The method of claim 261, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

264. The method of claim 261, wherein said relative skill level is determined to be relatively high if greater than a first threshold value, and relatively low if lower than a second threshold value.

265. The method of claim 261, wherein said reward algorithm and said penalty algorithm are linear.

266. The method of claim 261, further comprising modifying said reward algorithm and said penalty algorithm based on said successful game move determination.

267. The method of claim 261, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

268. The method of claim 267, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

269. The method of claim 267, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

270. The method of claim 267, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

271. The method of claim 267, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

272. The method of claim 261, wherein said game move probability distribution is updated using a learning automaton.

273. A method of matching a skill level of game player with a skill level of a computer game, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining if said selected game move is successful;

determining a current skill level of said game player relative to a current skill level of said computer game;

generating a successful outcome if said selected game move is successful and said relative skill level is relatively high, or if said selected game move is unsuccessful and said relative skill level is relatively low; and

updating said game move probability distribution based on said successful outcome.

274. The method of claim 273, wherein said selected game move is selected in response to said identified player move.

275. The method of claim 273, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

276. The method of claim 273, wherein said relative skill level is determined to be relatively high if greater than a first threshold value, and relatively low if lower than a second threshold value.

277. The method of claim 273, wherein said successful outcome equals the value “

1.”

278. The method of claim 273, wherein said successful outcome equals the value “

0.”

279. The method of claim 273, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

280. The method of claim 279, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

281. The method of claim 279, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

282. The method of claim 279, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

283. The method of claim 279, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

284. The method of claim 273, wherein said game move probability distribution is updated using a learning automaton.

285. A method of matching a skill level of game player with a skill level of a computer game, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining if said selected game move is successful;

determining a current skill level of said game player relative to a current skill level of said computer game;

generating an unsuccessful outcome if said selected game move is unsuccessful and said relative skill level is relatively high, or if said selected game move is successful and said relative skill level is relatively low; and

updating said game move probability distribution based on said unsuccessful outcome.

286. The method of claim 285, wherein said selected game move is selected in response to said identified player move.

287. The method of claim 285, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

288. The method of claim 285, wherein said relative skill level is determined to be relatively high if greater than a first threshold value, and relatively low if lower than a second threshold value.

289. The method of claim 285, wherein said unsuccessful outcome equals the value “

1.”

290. The method of claim 285, wherein said unsuccessful outcome equals the value “

0.”

291. The method of claim 285, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

292. The method of claim 291, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

293. The method of claim 291, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

294. The method of claim 291, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

295. The method of claim 291, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

296. The method of claim 285, wherein said game move probability distribution is updated using a learning automaton.

297. A method of matching a skill level of game player with a skill level of a computer game, comprising:

identifying a move performed by said game player;

selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;

determining if said selected game move is successful;

determining a current skill level of said game player relative to a current skill level of said computer game;

generating a successful outcome if said selected game move is successful and said relative skill level is relatively high, or if said selected game move is successful and said relative skill level is relatively low;

generating an unsuccessful outcome if said selected game move is unsuccessful and said relative skill level is relatively high, or if said selected game move is successful and said relative skill level is relatively low; and

updating said game move probability distribution based on said successful outcome and said unsuccessful outcome.

298. The method of claim 297, wherein said selected game move is selected in response to said identified player move.

299. The method of claim 297, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

300. The method of claim 297, wherein said relative skill level is determined to be relatively high if greater than a first threshold value, and relatively low if lower than a second threshold value.

301. The method of claim 297, wherein said successful outcome equals the value “

1”

, and said unsuccessful outcome equal the value “

0.”

302. The method of claim 297, wherein said successful outcome equals the value “

0,” and

said unsuccessful outcome equal the value “

1.”

303. The method of claim 297, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

304. The method of claim 303, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

305. The method of claim 303, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

306. The method of claim 303, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

307. The method of claim 303, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

308. The method of claim 297, wherein said game move probability distribution is updated using a learning automaton.

309. A method of providing learning capability to a processing device, comprising:

generating an action probability distribution comprising a plurality of probability values corresponding to a plurality of processor actions; and

transforming said action probability distribution.

310. The method of claim 309, further comprising:

identifying an action performed by a user;

selecting one of said plurality of processor actions;

determining an outcome of said selected processor action relative to said identified user action; and

updating said action probability distribution prior to said action probability distribution transformation, said action probability distribution update being based on said outcome.

311. The method of claim 310, wherein said selected user action is selected in response to said processor action.

312. The method of claim 309, wherein said processing device has one or more objectives, the method further comprising determining a performance index indicative of a performance of said processing device relative to said one or more objectives, wherein said action probability distribution transformation is based on said performance index.

313. The method of claim 309, wherein said transformation is performed deterministically.

314. The method of claim 309, wherein said transformation is performed modified quasi-deterministically.

315. The method of claim 309, wherein said transformation is performed probabilistically.

316. The method of claim 309, wherein said action probability distribution transformation comprises assigning a value to one or more of said plurality of probability values.

317. The method of claim 309, wherein said action probability distribution transformation comprises switching a higher probability value and a lower probability value.

318. The method of claim 309, wherein said action probability distribution transformation comprises switching a set of highest probability values and a set lowest probability values.

319. The method of claim 309, wherein said action probability distribution is updated using a learning automaton.

320. A method of providing learning capability to a computer game, comprising:

generating a game move probability distribution comprising a plurality of probability values corresponding to a plurality of game moves; and

transforming said game move probability distribution.

321. The method of claim 320, further comprising:

identifying a move performed by a game player;

selecting one of said plurality of game moves;

determining an outcome of said selected game move relative to said identified player move; and

updating said game move probability distribution prior to said game move probability distribution transformation, said game move probability distribution update being based on said outcome.

322. The method of claim 321, wherein said selected game move is selected in response to said identified player move.

323. The method of claim 320, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

324. The method of claim 323, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

325. The method of claim 323, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

326. The method of claim 323, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

327. The method of claim 323, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

328. The method of claim 320, wherein said transformation is performed deterministically.

329. The method of claim 320, wherein said transformation is performed modified quasi- deterministically.

330. The method of claim 320, wherein said transformation is performed probabilistically.

331. The method of claim 320, wherein said game move probability distribution transformation comprises assigning a value to one or more of said plurality of probability values.

332. The method of claim 320, wherein said game move probability distribution transformation comprises switching a higher probability value and a lower probability value.

333. The method of claim 320, wherein said game move probability distribution transformation comprises switching a set of highest probability values and a set lowest probability values.

334. The method of claim 320, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

335. The method of claim 320, wherein said game move probability distribution is transformed based on a skill level of a game player relative to a skill level of said computer game.

336. The method of claim 335, wherein said game move probability distribution transformation comprises switching a higher probability value and a lower probability value if said relative skill level is greater than a threshold level.

337. The method of claim 335, wherein said game move probability distribution transformation comprises switching a set of highest probability values and a set of lowest probability values if said relative skill level is greater than a threshold level.

338. The method of claim 335, wherein said game move probability distribution transformation comprises switching a higher probability value and a lower probability value if said relative skill level is less than a threshold level.

339. The method of claim 335, wherein said game move probability distribution transformation comprises switching a set of highest probability values and a set of lowest probability values if said relative skill level is less than a threshold level.

340. The method of claim 320, wherein said game move probability distribution is updated using a learning automaton.

341. A method of providing learning capability to a processing device, comprising:

generating an action probability distribution comprising a plurality of probability values corresponding to a plurality of processor actions; and

limiting one or more of said plurality of probability values.

342. The method of claim 341, further comprising:

identifying an action performed by a user;

selecting one of said plurality of processor actions;

determining an outcome of one or more said identified user action and said selected processor action; and

updating said action probability distribution based on said outcome.

343. The method of claim 342, wherein said outcome is of said identified user action.

344. The method of claim 342, wherein said outcome is of said selected processor action.

345. The method of claim 342, wherein said outcome is of said selected processor action relative to said identified user action.

346. The method of claim 342, wherein said selected user action is selected in response to said processor action.

347. The method of claim 341, wherein said processing device has one or more objectives, the method further comprising determining a performance index indicative of a performance of said processing device relative to said one or more objectives, wherein said probability value limitation is based on said performance index.

348. The method of claim 341, wherein said one or more probability values are limited to a high value.

349. The method of claim 341, wherein said one or more probability values are limited to a low value.

350. The method of claim 341, wherein said plurality of probability values is limited.

351. The method of claim 341, wherein said action probability distribution is updated using a learning automaton.

352. A method of providing learning capability to a computer game, comprising:

generating a game move probability distribution comprising a plurality of probability values corresponding to a plurality of game moves; and

limiting one or more of said plurality of probability values.

353. The method of claim 352, further comprising:

identifying a move performed by a game player;

selecting one of said plurality of game moves;

determining an outcome of said selected game move relative to said identified player move; and

updating said game move probability distribution based on said outcome.

354. The method of claim 353, wherein said selected game move is selected in response to said identified player move.

355. The method of claim 353, wherein said plurality of game moves is performed by a game-manipulated object, and said identified player move is performed by a user-manipulated object.

356. The method of claim 353, wherein said plurality of game moves comprises discrete movements of said game-manipulated object.

357. The method of claim 353, wherein said plurality of game moves comprises a plurality of delays related to a movement of said game-manipulated object.

358. The method of claim 353, wherein said identified player move comprises a simulated shot taken by said user-manipulated object.

359. The method of claim 353, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

360. The method of claim 352, wherein said one or more probability values are limited to a high value.

361. The method of claim 352, wherein said one or more probability values are limited to a low value.

362. The method of claim 352, wherein said plurality of probability values is limited.

363. The method of claim 352, wherein said one or more probability values is limited based on a skill level of a game player relative to a skill level of said computer game.

364. The method of claim 363, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

365. The method of claim 352, wherein said game move probability distribution is updated using a learning automaton.

366. A method of providing learning capability to a processing device, comprising:

identifying an action performed by a user;

selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

determining an outcome of one or both of said identified user action and said selected processor action;

updating said action probability distribution based on said outcome; and

wherein said action probability distribution is prevented from substantially converging to a single probability value.

367. The method of claim 366, wherein said outcome is of said identified user action.

368. The method of claim 366, wherein said outcome is of said selected processor action.

369. The method of claim 366, wherein said outcome is of said selected processor action relative to said identified user action.

370. The method of claim 366, wherein said selected processor action is selected in response to said identified user action.

371. The method of claim 366, wherein said outcome can be represented by one of two possible values.

372. The method of claim 371, wherein said two possible values are the integers “

zero” and

“

one.”

373. The method of claim 366, wherein said outcome can be represented by one of a finite range of real numbers.

374. The method of claim 366, wherein said outcome can be represented by one of a range of continuous values.

375. The method of claim 366, wherein said selected processor action is a currently selected processor action relative to said action probability distribution update.

376. The method of claim 366, wherein said selected processor action is a previously selected processor action relative to said action probability distribution update.

377. The method of claim 366, wherein said selected processor action is a subsequently selected processor action relative to said action probability distribution update.

378. The method of claim 366, further comprising initially generating said action probability distribution with equal probability values.

379. The method of claim 366, further comprising initially generating said action probability distribution with unequal probability values.

380. The method of claim 366, wherein said action probability distribution update comprises a linear update.

381. The method of claim 366, wherein said action probability distribution update comprises a nonlinear update.

382. The method of claim 366, wherein said action probability distribution update comprises an absolutely expedient update.

383. The method of claim 366, wherein said action probability distribution update comprises a reward-penalty update.

384. The method of claim 366, wherein said action probability distribution update comprises a reward-inaction update.

385. The method of claim 366, wherein said action probability distribution update comprises a inaction-penalty update.

386. The method of claim 366, wherein said action probability distribution is normalized.

387. The method of claim 366, wherein said selected processor action corresponds to the highest probability value within said action probability distribution.

388. The method of claim 366, wherein said selected processor action corresponds to a pseudo-random selection of a probability value within said action probability distribution.

389. The method of claim 366, wherein said processing device is a computer game, said identified user action is a player move, and said processor actions are game moves.

390. The method of claim 366, wherein said processing device is an educational toy, said identified user action is a child action, and said processor actions are toy actions.

391. The method of claim 366, wherein said processing device is a telephone system, said identified user action is a called phone number, and said processor actions are listed phone numbers.

392. The method of claim 366, wherein said processing device is a television channel control system, said identified user action is a watched television channel, and said processor actions are listed television channels.

393. The method of claim 366, wherein said action probability distribution is updated using a learning automaton.

394. A processing device, comprising:

a probabilistic learning module configured for learning a plurality of processor actions in response to a plurality of actions performed by a user; and

an intuition module configured for preventing said probabilistic learning module from substantially converging to a single processor action.

395. The processing device of claim 394, wherein said intuition module is deterministic.

396. The processing device of claim 394, wherein said intuition module is quasi-deterministic.

397. The processing device of claim 394, wherein said intuition module is probabilistic.

398. The processing device of claim 394, wherein said intuition module comprises artificial intelligence.

399. The processing device of claim 394, wherein said intuition module comprises an expert system.

400. The processing device of claim 394, wherein said intuition module comprises a neural network.

401. The processing device of claim 394, wherein said intuition module comprises fuzzy logic.

402. The processing device of claim 394, wherein said probabilistic learning module comprises:

an action selection module configured for selecting one of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

an outcome evaluation module configured for determining an outcome of one or both of said identified user action and said selected processor action; and

a probability update module configured for updating said action probability distribution based on said outcome.

403. The processing device of claim 402, wherein said outcome is of said identified user action.

404. The processing device of claim 402, wherein said outcome is of said selected processor action.

405. The processing device of claim 402, wherein said outcome is of said selected processor action relative to said identified user action.

406. The processing device of claim 394, wherein said probabilistic learning module comprises a learning automaton.

407-1. The method of claim 407, wherein said outcome can be represented by one of two possible values.

408-2. The method of claim 409, wherein said two possible values are the integers “

zero” and

“

one.”

409-3. The method of claim 407, wherein said outcome can be represented by one of a range of continuous values.

429. A processing device having a function independent of determining an optimum action, comprising:

an action selection module configured for selecting one of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions, wherein said selected processor action affects said processing device function;

an outcome evaluation module configured for determining an outcome of one or both of said identified user action and said selected processor action; and

a probability update module configured for updating said action probability distribution based on said outcome.

430. The processing device of claim 429, wherein said outcome is of said identified user action.

431. The processing device of claim 429, wherein said outcome is of said selected processor action.

432. The processing device of claim 429, wherein said outcome is of said selected processor action relative to said identified user action.

433. The processing device of claim 429, wherein said processing device is a computer game.

434. The processing device of claim 429, wherein said processing device is an educational toy.

435. The processing device of claim 429, wherein said processing device is a telephone system.

436. The processing device of claim 429, wherein said processing device is a television channel control system.

437. The processing device of claim 429, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.

438. A method of providing learning capability to a processing device having one or more objectives, comprising:

identifying actions from a plurality of users;

selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

determining one or more outcomes of one or both of said identified plurality of user actions and said selected one or more processor actions;

updating said action probability distribution using one or more learning automatons based on said one or more outcomes; and

modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

439. The method of claim 438, wherein said one or more outcome are of said identified plurality of user actions.

440. The method of claim 438, wherein said one or more outcome are of said selected one or more processor actions.

441. The method of claim 438, wherein said one or more outcome are of said selected one or more processor actions relative to said plurality of identified plurality of user actions.

442. The method of claim 438, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

443. The method of claim 438, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

444. The method of claim 438, wherein said one or more outcomes comprises a single outcome corresponding to said identified plurality of user actions.

445. The method of claim 438, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to said identified plurality of user actions.

446. The method of claim 438, wherein said action probability distribution is updated when a predetermined period of time has expired.

447. The method of claim 438, wherein said action probability distribution is updated in response to said identification of each user action.

448. The method of claim 438, wherein said selected processor action is selected in response to said identified plurality of user actions.

449. The method of claim 438, further comprising determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

450. The method of claim 449, wherein said one or more performance indexes comprises a single performance index corresponding to said identified plurality of user actions.

451. The method of claim 449, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said identified plurality of user actions.

452. The method of claim 438, wherein said modification comprises modifying said action selection.

453. The method of claim 438, wherein said modification comprises modifying said outcome determination.

454. The method of claim 438, wherein said modification comprises modifying said action probability distribution update.

455. The method of claim 438, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

456. The method of claim 438, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

457. The method of claim 438, wherein outcome determination is performed only after several iterations of said identified user action identification and processor action selection.

458. The method of claim 438, wherein said probability distribution update is performed only after several iterations of said identified user action identification and processor action selection.

459. The method of claim 438, wherein said probability distribution update is performed only after several iterations of said identified user action identification, processor action selection, and outcome determination.

460. The method of claim 438, wherein said processing device is a computer game, said identified user actions are player moves, and said processor actions are game moves.

461. A method of providing learning capability to a processing device having one or more objectives, comprising:

identifying actions from users divided amongst a plurality of user sets;

for each of said user sets;

selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

determining one or more outcomes of one or more actions from said each user set and said selected one or more processor actions;

updating said action probability distribution using a learning automaton based on said one or more outcomes; and

modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

462. The method of claim 461, wherein each user set comprises a single user.

463. The method of claim 461, wherein each user set comprises a plurality of users.

464. The method of claim 463, wherein said selected one or more processor actions comprises a single processor action corresponding to actions from said plurality of users.

465. The method of claim 463, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to actions from said plurality of users.

466. The method of claim 463, wherein said one or more outcomes comprises a single outcome corresponding to actions from said plurality of users.

467. The method of claim 463, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to actions from said plurality of users.

468. The method of claim 461, wherein said action probability distribution is updated when a predetermined period of time has expired.

469. The method of claim 461, wherein said action probability distribution is updated in response to said identification of each user action.

470. The method of claim 461, wherein said selected one or more processor actions is selected in response to said identified user actions.

471. The method of claim 461, further comprising determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

472. The method of claim 463, further comprising determining a single performance index indicative of a performance of said processing device relative to said one or more objectives, wherein said single performance index corresponds to said identified plurality of user actions and said modification is based on said single performance index.

473. The method of claim 463, further comprising determining a plurality of performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said plurality of performance indexes corresponds to said identified plurality of user actions and said modification is based on said plurality of performance indexes.

474. The method of claim 461, wherein said modification comprises modifying said action selection.

475. The method of claim 461, wherein said modification comprises modifying said outcome determination.

476. The method of claim 461, wherein said modification comprises modifying said action probability distribution update.

477. The method of claim 461, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

478. The method of claim 461, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

479. The method of claim 461, wherein outcome determination is performed only after several iterations of said identified user action identification and processor action selection.

480. The method of claim 461, wherein said probability distribution update is performed only after several iterations of said identified user action identification and processor action selection.

481. The method of claim 461, wherein said probability distribution update is performed only after several iterations of said identified user action identification, processor action selection, and outcome determination.

482. The method of claim 461, wherein said processing device is a computer game, said identified user actions are player moves, and said processor actions are game moves.

483. The method of claim 461, wherein said processing device is a telephone system, said identified user actions are called phone numbers, and said processor actions are listed phone numbers.

484. A processing device having one or more objectives, comprising:

a probabilistic learning module having a learning automaton configured for learning a plurality of processor actions in response to a plurality of actions performed by a plurality of users; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said one or more objectives.

485. The processing device of claim 484, wherein said intuition module is further configured for determining one or more performance indexes indicative of a performance of said probabilistic learning module relative to said one or more objectives, and for modifying said probabilistic learning module functionality based on said one or more performance indexes.

486. The processing device of claim 485, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of users.

487. The processing device of claim 485, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of users.

488. The processing device of claim 484, wherein said intuition module is configured for selecting one of a predetermined plurality of algorithms employed by said learning module.

489. The processing device of claim 484, wherein said intuition module is configured for modifying a parameter of an algorithm employed by said learning module.

490. The processing device of claim 484, wherein said probabilistic learning module comprises:

one or more action selection modules configured for selecting one or more of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

one or more outcome evaluation modules configured for determining one or more outcomes of one or both of said identified plurality of user actions and said selected one or more processor actions; and

a probability update module configured for updating said action probability distribution based on said one or more outcomes.

491. The processing device of claim 490, wherein said one or more outcome are of said identified plurality of user actions.

492. The processing device of claim 490, wherein said one or more outcome are of said selected one or more processor actions.

493. The processing device of claim 490, wherein said one or more outcome are of said selected one or more processor actions relative to said plurality of identified plurality of user actions.

494. The processing device of claim 490, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

495. The processing device of claim 490, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

496. The processing device of claim 490, wherein said one or more outcomes comprises a single outcome corresponding to said identified plurality of user actions.

497. The processing device of claim 490, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to said identified plurality of user actions.

498. The processing device of claim 490, wherein said intuition module is configured for modifying a functionality of said one or more action selection modules based on said one or more objectives.

499. The processing device of claim 490, wherein said intuition module is configured for modifying a functionality of said one or more outcome evaluation modules based on said one or more objectives.

500. The processing device of claim 490, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

501. The processing device of claim 490, further comprising:

a server containing said one or more action selection modules, said one or more outcome evaluation modules, and said probability update module;

a plurality of computers configuring for respectively generating said identified plurality of user actions; and

a network configured for transmitting said identified plurality of user actions from said plurality of computers to said server and for transmitting said selected one or more processor actions from said server to said plurality of computers.

502. The processing device of claim 490, wherein said one or more action selection modules comprises a plurality of action selection modules, and said selected one or more processor actions comprises a plurality of processor actions, the processing device further comprising:

a server containing said one or more outcome evaluation modules, and said probability update module;

a plurality of computers configuring for respectively generating said identified plurality of user actions, said plurality of computers respectively containing said plurality of action selection modules; and

a network configured for transmitting said identified plurality of user actions and said selected plurality of processor actions from said plurality of computers to said server.

503. The processing device of claim 490, wherein said one or more action selection modules comprises a plurality of action selection modules, said selected one or more processor actions comprises a plurality of processor actions, said one or more outcome evaluation modules comprises a plurality of outcome evaluation modules, and said one or more outcomes comprises a plurality of outcomes, the processing device further comprising:

a server containing said probability update module;

a plurality of computers configuring for respectively generating said identified plurality of user actions, said plurality of computers respectively containing said plurality of action selection modules and said plurality of outcome evaluation modules; and

a network configured for transmitting said plurality of outcomes from said plurality of computers to said server.

504. The processing device of claim 484, wherein said plurality of users are divided amongst a plurality of user sets, and wherein said probabilistic learning module comprises:

one or more action selection modules configured for, each user set, selecting one or more of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

one or more outcome evaluation modules configured for, for said each user set, determining one or more outcomes of one or both of one or more user actions and said selected one or more processor actions; and

one or more probability update modules configured for, for said each user set, updating said action probability distribution based on said one or more outcomes.

505. The processing device of claim 504, wherein said one or more outcome are of said identified plurality of user actions.

506. The processing device of claim 504, wherein said one or more outcome are of said selected one or more processor actions.

507. The processing device of claim 504, wherein said one or more outcome are of said selected one or more processor actions relative to said plurality of identified plurality of user actions.

508. The processing device of claim 504, wherein each user set comprises a single user.

509. The processing device of claim 504, wherein each user set comprises a plurality of users.

510. The processing device of claim 504, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

511. The processing device of claim 504, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

512. The processing device of claim 504, wherein said intuition module is configured for modifying a functionality of said one or more action selection modules based on said one or more objectives.

513. The processing device of claim 504, wherein said intuition module is configured for modifying a functionality of said one or more outcome evaluation modules based on said one or more objectives.

514. The processing device of claim 504, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

515. The processing device of claim 504, further comprising:

a server containing said one or more action selection modules, said one or more outcome evaluation modules, and said one or more probability update modules;

a plurality of computers configuring for respectively generating said identified plurality of user actions; and

a network configured for transmitting said identified plurality of user actions from said plurality of computers to said server and for transmitting said selected one or more processor actions from said server to said plurality of computers.

516. The processing device of claim 504, wherein said one or more action selection modules comprises a plurality of action selection modules, and said selected one or more processor actions comprises a plurality of processor actions, the processing device further comprising:

a server containing said one or more outcome evaluation modules and said one or more probability update modules;

a plurality of computers configuring for respectively generating said identified plurality of user actions, said plurality of computers respectively containing said plurality of action selection modules; and

a network configured for transmitting said identified plurality of user actions and said selected plurality of processor actions from said plurality of computers to said server.

517. The processing device of claim 504, wherein said one or more action selection modules comprises a plurality of action selection modules, said selected one or more processor actions comprises a plurality of processor actions, said one or more outcome evaluation modules comprises a plurality of outcome evaluation modules, and said one or more outcomes comprises a plurality of outcomes, the processing device further comprising:

a server containing said one or more probability update modules;

a plurality of computers configuring for respectively generating said identified plurality of user actions, said plurality of computers respectively containing said plurality of action selection modules and said plurality of outcome evaluation modules; and

a network configured for transmitting said plurality of outcomes from said plurality of computers to said server.

518. The processing device of claim 503, wherein said one or more action selection modules comprises a plurality of action selection modules, said selected one or more processor actions comprises a plurality of processor actions, said one or more outcome evaluation modules comprises a plurality of outcome evaluation modules, and said one or more outcomes comprises a plurality of outcomes, said one or more probability update modules comprises a plurality of update modules for updating said plurality of action probability distributions, the processing device further comprising:

a server containing said a module for generating a centralized action probability distribution based on said plurality of action probability distributions, said centralized action probability distribution used to initialize a subsequent plurality of action probability distributions;

a plurality of computers configuring for respectively generating said identified plurality of user actions, said plurality of computers respectively containing said plurality of action selection modules, said plurality of outcome evaluation modules, and said plurality of probability update modules; and

a network configured for transmitting said plurality of action probability distributions from said plurality of computers to said server, and said centralized action probability distribution from said server to said plurality of computers.

519. A method of providing learning capability to a processing device having one or more objectives, comprising:

identifying a plurality of user actions;

selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

weighting said identified plurality of user actions;

determining one or more outcomes of said plurality of weighted user actions; and

updating said action probability distribution based on said one or more outcomes.

520. The method of claim 519, wherein said identified plurality of user actions is received from a plurality of users.

521. The method of claim 520, wherein said weighting is based on a skill level of said plurality of users.

522. The method of claim 519, wherein said one or more selected processor actions is selected in response to said identified plurality of user actions.

523. The method of claim 519, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

524. The method of claim 519, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

525. The method of claim 519, wherein said one or more outcomes comprises a single outcome corresponding to said identified plurality of user actions.

526. The method of claim 519, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to said identified plurality of user actions.

527. The method of claim 519, further comprising modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

528. The method of claim 527, further comprising determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

529. The method of claim 527, wherein said one or more performance indexes comprises a single performance index corresponding to said identified plurality of user actions.

530. The method of claim 527, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said identified plurality of user actions.

531. The method of claim 527, wherein said modification comprises modifying said action selection.

532. The method of claim 527, wherein said modification comprises modifying said outcome determination.

533. The method of claim 532, wherein said outcome determination modification comprises modifying said weighting of said identified plurality of user actions.

534. The method of claim 527, wherein said modification comprises modifying said action probability distribution update.

535. The method of claim 527, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

536. The method of claim 527, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

537. The method of claim 519, wherein said action probability distribution is updated using a learning automaton.

538. The method of claim 519, wherein said processing device is a computer game, said identified user actions are player moves, and said processor actions are game moves.

539. A processing device having one or more objectives, comprising:

an action selection module configured for selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

an outcome evaluation module configured for weighting a plurality of identified user actions, and for determining one or more outcomes of said plurality of weighted user actions; and

a probability update module configured for updating said action probability distribution based on said outcome.

540. The processing device of claim 539, wherein said identified plurality of user actions is received from a plurality of users.

541. The processing device of claim 540, wherein said weighting is based on a skill level of said plurality of users.

542. The processing device of claim 539, wherein said action selection module is configured for selecting said one or more selected processor actions in response to said identified plurality of user actions.

543. The processing device of claim 539, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

544. The processing device of claim 539, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

545. The processing device of claim 539, wherein said one or more outcomes comprises a single outcome corresponding to said identified plurality of user actions.

546. The processing device of claim 539, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to said identified plurality of user actions.

547. The processing device of claim 539, further comprising an intuition module configured for modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

548. The processing device of claim 547, wherein said intuition module is further configured for determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

549. The processing device of claim 547, wherein said one or more performance indexes comprises a single performance index corresponding to said identified plurality of user actions.

550. The processing device of claim 547, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said identified plurality of user actions.

551. The processing device of claim 547, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

552. The processing device of claim 547, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

553. The processing device of claim 547, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

554. The processing device of claim 539, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.

555. A method of providing learning capability to a processing device having one or more objectives, comprising:

identifying a plurality of user actions;

selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

determining a success ratio of said selected processor action relative to said identified plurality of user actions;

comparing said determined success ratio to a reference success ratio;

determining an outcome of said success ratio comparison; and

updating said action probability distribution based on said outcome.

556. The method of claim 555, wherein said identified plurality of user actions is received from a plurality of users.

557. The method of claim 555, wherein said identified plurality of user actions is received from a single user.

558. The method of claim 555, wherein said reference success ratio is a simple majority.

559. The method of claim 555, wherein said reference success ratio is a minority.

560. The method of claim 555, wherein said reference success ratio is a super majority.

561. The method of claim 555, wherein said reference success ratio is a unanimity.

562. The method of claim 555, wherein said reference success ratio is an equality.

563. The method of claim 555, wherein said selected processor action is selected in response to said identified plurality of user actions.

564. The method of claim 555, further comprising modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

565. The method of claim 564, further comprising determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

566. The method of claim 564, wherein said modification comprises modifying said reference success ratio.

567. The method of claim 564, wherein said modification comprises modifying said action selection.

568. The method of claim 564, wherein said modification comprises modifying said outcome determination.

569. The method of claim 564, wherein said modification comprises modifying said action probability distribution update.

570. The method of claim 564, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

571. The method of claim 564, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

572. The method of claim 555, wherein said action probability distribution is updated using a learning automaton.

573. The method of claim 555, wherein said processing device is a computer game, said identified user actions are player moves, and said processor actions are game moves.

574. A processing device having one or more objectives, comprising:

an action selection module configured for selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

an outcome evaluation module configured for determining a success ratio of said selected processor action relative to a plurality of user actions, for comparing said determined success ratio to a reference success ratio, and for determining an outcome of said success ratio comparison; and

a probability update module configured for updating said action probability distribution based on said outcome.

575. The processing device of claim 574, wherein said identified plurality of user actions is received from a plurality of users.

576. The processing device of claim 574, wherein said identified plurality of user actions is received from a single user.

577. The processing device of claim 574, wherein said reference success ratio is a simple majority.

578. The processing device of claim 574, wherein said reference success ratio is a minority.

579. The processing device of claim 574, wherein said reference success ratio is a super majority.

580. The processing device of claim 574, wherein said reference success ratio is a unanimity.

581. The processing device of claim 574, wherein said reference success ratio is an equality.

582. The processing device of claim 574, wherein said action selection module is configured for selecting said processor action in response to said identified plurality of user actions.

583. The processing device of claim 574, further comprising an intuition module configured for modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

584. The processing device of claim 583, wherein said intuition module is further configured for determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

585. The processing device of claim 583, wherein said one or more performance indexes comprises a single performance index corresponding to said identified plurality of user actions.

586. The processing device of claim 583, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said identified plurality of user actions.

587. The processing device of claim 583, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

588. The processing device of claim 583, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

589. The processing device of claim 583, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

590. The processing device of claim 574, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.

591. A method of providing learning capability to a processing device having one or more objectives, comprising:

identifying actions from a plurality of users;

selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

determining if said selected processor action has a relative success level for a majority of said plurality of users;

determining an outcome of said success determination; and

updating said action probability distribution based on said outcome.

592. The method of claim 591, wherein said reference success level is a greatest success.

593. The method of claim 591, wherein said reference success level is a least success.

594. The method of claim 591, wherein said reference success level is an average success.

595. The method of claim 591, further comprising maintaining separate action probability distributions for said plurality of users, wherein said relative success level of said selected processor action is determined from said separate action probability distributions.

596. The method of claim 591, further comprising maintaining an estimator success table for said plurality of users, wherein said relative success level of said selected processor action is determined from said estimator table.

597. The method of claim 591, wherein said selected processor action is selected in response to said identified plurality of user actions.

598. The method of claim 591, further comprising modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

599. The method of claim 598, further comprising determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

600. The method of claim 598, wherein said modification comprises modifying said relative success level.

601. The method of claim 598, wherein said modification comprises modifying said action selection.

602. The method of claim 598, wherein said modification comprises modifying said outcome determination.

603. The method of claim 598, wherein said modification comprises modifying said action probability distribution update.

604. The method of claim 598, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

605. The method of claim 598, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

606. The method of claim 591, wherein said action probability distribution is updated using a learning automaton.

607. The method of claim 591, wherein said processing device is a computer game, said identified user actions are player moves, and said processor actions are game moves.

608. A processing device having one or more objectives, comprising:

an action selection module configured for selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;

an outcome evaluation module configured for determining if said selected processor action has a relative success level for a majority of a plurality of users, and for determining an outcome of said success determination; and

a probability update module configured for updating said action probability distribution based on said outcome.

609. The processing device of claim 608, wherein said reference success level is a greatest success.

610. The processing device of claim 608, wherein said reference success level is a least success.

611. The processing device of claim 608, wherein said reference success level is an average success.

612. The processing device of claim 608, wherein said probability update module is further configured for maintaining separate action probability distributions for said plurality of users, and said outcome evaluation module is configured for determining said relative success level of said selected processor action from said separate action probability distributions.

613. The processing device of claim 608, wherein said outcome evaluation module is further configured for maintaining an estimator success table for said plurality of users, and for determining said relative success level of said selected processor action from said estimator table.

614. The processing device of claim 608, wherein said action selection module is configured for selecting said selected processor action in response to said identified plurality of user actions.

615. The processing device of claim 608, further comprising an intuition module configured for modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

616. The processing device of claim 615, wherein said intuition module is further configured for determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

617. The processing device of claim 615, wherein said one or more performance indexes comprises a single performance index corresponding to said identified plurality of user actions.

618. The processing device of claim 615, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said identified plurality of user actions.

619. The processing device of claim 615, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

620. The processing device of claim 615, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

621. The processing device of claim 615, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

622. The processing device of claim 608, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.

623. A method of providing learning capability to a processing device having one or more objectives, comprising:

selecting one or more of a plurality of processor actions that are respectively linked to a plurality of user parameters, said selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of linked processor actions;

linking said one or more selected process actions with one or more of said plurality of user parameters;

determining one or more outcomes of said one or more linked processor actions; and

updating said action probability distribution based on said one or more outcomes.

624. The method of claim 623, wherein said plurality of user parameters comprises a plurality of user actions.

625. The method of claim 623, wherein said plurality of user parameters comprises a plurality of users.

626. The method of claim 623, wherein said plurality of processor actions is linked to another plurality of user parameters.

627. The method of claim 626, wherein said plurality of user parameters comprises a plurality of user actions, and said other plurality of user parameters comprises a plurality of users.

628. The method of claim 623, further comprising identifying one or more user actions.

629. The method of claim 628, wherein said selected one or more processor actions is selected in response to said one or more user actions.

630. The method of claim 628, wherein said one or more user actions comprises a plurality of user actions.

631. The method of claim 630, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

632. The method of claim 630, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

633. The method of claim 630, wherein said one or more outcomes comprises a single outcome corresponding to said identified plurality of user actions.

634. The method of claim 630, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to said identified plurality of user actions.

635. The method of claim 623, further comprising modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

636. The method of claim 635, further comprising determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

637. The method of claim 635, wherein said modification comprises modifying said action selection.

638. The method of claim 635, wherein said modification comprises modifying said outcome determination.

639. The method of claim 635, wherein said modification comprises modifying said action probability distribution update.

640. The method of claim 635, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

641. The method of claim 635, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more of said processor action selection, said outcome determination, and said action probability distribution update.

642. The method of claim 623, wherein said action probability distribution is updated using a learning automaton.

643. The method of claim 623, wherein said processing device is a computer game, said one or more user actions are one or more player moves, and said processor actions are game moves.

644. A processing device having one or more objectives, comprising:

an action selection module configured for selecting one or more of a plurality of processor actions that are respectively linked to a plurality of user parameters, said selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of linked processor actions;

an outcome evaluation module configured for linking said one or more selected process actions with one or more of said plurality of user parameters, and for determining one or more outcomes of said one or more linked processor actions and one or more user actions; and

a probability update module configured for updating said action probability distribution based on said one or more outcomes.

645. The processing device of claim 644, wherein said plurality of user parameters comprises a plurality of user actions.

646. The processing device of claim 644, wherein said plurality of user parameters comprises a plurality of users.

647. The processing device of claim 644, wherein said outcome evaluation module is configured for linking said plurality of processor actions to another plurality of user parameters.

648. The processing device of claim 647, wherein said plurality of user parameters comprises a plurality of user actions, and said other plurality of user parameters comprises a plurality of users.

649. The processing device of claim 644, wherein said action selection module is configured for selecting said selected one or more processor actions in response to said one or more user actions.

650. The processing device of claim 644, wherein said one or more user actions comprises a plurality of user actions.

651. The processing device of claim 650, wherein said selected one or more processor actions comprises a single processor action corresponding to said identified plurality of user actions.

652. The processing device of claim 650, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said identified plurality of user actions.

653. The processing device of claim 650, wherein said one or more outcomes comprises a single outcome corresponding to said identified plurality of user actions.

654. The processing device of claim 650, wherein said one or more outcomes comprises a plurality of outcomes respectively corresponding to said identified plurality of user actions.

655. The processing device of claim 644, further comprising an intuition module configured for modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

656. The processing device of claim 655, wherein said intuition module is further configured for determining one or more performance indexes indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said one or more performance indexes.

657. The processing device of claim 655, wherein said one or more performance indexes comprises a single performance index corresponding to said identified plurality of user actions.

658. The processing device of claim 655, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said identified plurality of user actions.

659. The processing device of claim 655, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

660. The processing device of claim 655, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

661. The processing device of claim 655, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

662. The processing device of claim 644, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.

663. A method of providing learning capability to a processing device having an objective, comprising:

generating a list containing a plurality of listed items with an associated item probability distribution comprising a plurality of probability values corresponding to said plurality of listed items;

selecting one or more items from said plurality of listed items based on said item probability distribution;

determining a performance index indicative of a performance of said processing device relative to said objective; and

modifying said item probability distribution based on said performance index.

664. The method of claim 663, further comprising identifying an action associated item;

and

determining if said identified item matches any listed item contained in said list, wherein said performance index is derived from said matching determination.

665. The method of claim 663, wherein said selected one or more items comprises a plurality of selected items.

666. The method of claim 663, wherein said selected one or more items comprises a single selected item.

667. The method of claim 663, wherein said selected one or more items corresponds to the highest one or more probability values in said item probability distribution.

668. The method of claim 663, further comprising placing said plurality of listed items in an order according to corresponding probability values.

669. The method of claim 663, wherein said item probability distribution is modified by updating said item probability distribution.

670. The method of claim 669, wherein said item probability distribution update comprises a reward-inaction update.

671. The method of claim 663, wherein said item probability distribution is modified by increasing a probability value.

672. The method of claim 663, wherein said item probability distribution is modified by adding a probability value.

673. The method of claim 672, wherein said item probability distribution is modified by replacing a probability value with said added probability value.

674. The method of claim 663, wherein said performance index is instantaneous.

675. The method of claim 663, wherein said performance index is cumulative.

676. The method of claim 663, wherein said item probability distribution is normalized.

677. The method of claim 664, wherein said item probability distribution is modified by updating it if said identified item matches said any listed item.

678. The method of claim 677, wherein said item probability distribution update comprises a reward-inaction update.

679. The method of claim 678, wherein a corresponding probability value is rewarded if said identified item matches said any listed item.

680. The method of claim 677, further comprising adding a listed item corresponding to said identified item to said list if said identified item does not match said any listed item, wherein said item probability distribution is modified by adding a probability value corresponding to said added listed item to said item probability distribution.

681. The method of claim 680, wherein another item on said list is replaced with said added listed item, and another probability value corresponding to said replaced listed item is replaced with said added probability value.

682. The method of claim 663, wherein said item probability distribution is modified by updating it only if said identified item matches an item within said selected one or more items.

683. The method of claim 682, wherein said item probability distribution update comprises a reward-inaction update.

684. The method of claim 683, wherein a corresponding probability value is rewarded if said identified item matches said any listed item.

685. The method of claim 682, wherein said item probability distribution is modified by increasing a corresponding probability value if said identified item matches a listed item that does not correspond to an item in said selected one or more items.

686. The method of claim 682, further comprising adding a listed item corresponding to said identified item to said list if said identified item does not match said any listed item, wherein said item probability distribution is modified by adding a probability value corresponding to said added listed item to said item probability distribution.

687. The method of claim 686, wherein another item on said list is replaced with said added listed item, and another probability value corresponding to said replaced listed item is replaced with said added probability value.

688. The method of claim 663, wherein said processing device comprises a phone, said identified item comprises a phone number associated with a phone call, said listed items comprises listed phone numbers, and said item probability distribution comprises a phone number probability distribution.

689. The method of claim 663, wherein said processing device is a television, said identified item comprises a television channel that is watched, said listed items comprises listed television channels, and said item probability distribution comprises a television channel probability distribution.

690. The method of claim 663, further comprising:

generating another list containing another plurality of listed items with another associated item probability distribution comprising another plurality of probability values corresponding to said other plurality of listed items; and

selecting another one or more items from said other plurality of items based on said other item probability distribution.

691. The method of claim 690, further comprising:

identifying another action associated item; and

determining if said identified item matches any listed item contained in said list;

identifying another action associated item; and

determining if said other identified item matches any listed item contained in said other list;

wherein said performance index is derived from said matching determinations.

692. The method of claim 690, further comprising:

identifying an action associate item;

determining current temporal information;

selecting one of said list and said other list based on said current temporal information determination; and

determining if said identified items matches any listed item contained in said selected list, wherein said performance index is derived from said determination.

693. The method of claim 692, wherein said temporal information is a day of the week.

694. The method of claim 692, wherein said temporal information is a time of the day.

695. The method of claim 663, wherein said item probability distribution is updated using a learning automaton.

696. The method of claim 663, wherein said item probability distribution is purely frequency based.

697. The method of claim 696, wherein said item probability distribution is based on a moving average.

698. The method of claim 663, wherein said list is one of a plurality of like lists corresponding to a plurality of users, the method further comprising determining which user performed said identified item, wherein said list corresponds with said determined user.

699. The method of claim 698, wherein said user determination is based on the operation of one of a plurality of keys associated with said processing device.

700. A processing device having an objective, comprising:

a probabilistic learning module configured for learning a plurality of favorite items of a user in response to identified user items; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said objective.

701. The processing device of claim 700, wherein said probabilistic learning module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and said intuition module is configured for modifying said probabilistic learning module functionality based on said performance index.

702. The processing device of claim 700, wherein said probabilistic learning module comprises:

an item selection module configured for selecting said one or more of said plurality of favorite items from a plurality of items contained in a list, said selection being based on an item probability distribution comprising a plurality of probability values corresponding to said plurality of listed items;

an outcome evaluation module configured for determining if an identified item matches any listed item contained in said list; and

a probability update module, wherein said intuition module is configured for modifying said probability update module based on said matching determination.

703. The processing device of claim 702, wherein said one or more favorite items comprises a plurality of favorite items.

704. The processing device of claim 702, wherein said one or more favorite items comprises a single favorite item.

705. The processing device of claim 702, wherein said one or more favorite items correspond to one or more of the highest probability values in said item probability distribution.

706. The processing device of claim 702, wherein said item selection module is further configured for placing said plurality of listed items in an order according to corresponding probability values.

707. The processing device of claim 702, wherein said intuition module is configured for modifying said probability update module by directing it to update said item probability distribution if said identified item matches said any listed item.

708. The processing device of claim 707, wherein said probability update module is configured for updating said item probability distribution using a reward-inaction algorithm.

709. The processing device of claim 708, wherein said probability update module is configured for rewarding a corresponding probability value.

710. The processing device of claim 707, wherein said intuition module is configured for modifying said probability update module by adding a listed item corresponding to said identified item to said list and adding a probability value corresponding to said added listed items to said item probability distribution if said identified item does not match said any listed item.

711. The processing device of claim 710, wherein another item on said list is replaced with said added listed item, and another probability value corresponding to said replaced listed item is replaced with said added probability value.

712. The processing device of claim 702, wherein said intuition module is configured for modifying said probability update module by directing it to update said item probability distribution only if said identified item matches a listed item corresponding to one of said favorite items.

713. The processing device of claim 712, wherein said probability update module is configured for updating said item probability distribution using a reward-inaction algorithm.

714. The processing device of claim 713, wherein said probability update module is configured for rewarding a corresponding probability value.

715. The processing device of claim 712, wherein said intuition module is configured for modifying said probability update module by increasing a corresponding probability value if said identified item matches a listed item that does not correspond to one of said one or more favorite items.

716. The processing device of claim 712, wherein said intuition module is configured for modifying said probability update module by adding a listed item corresponding to said identified item to said list and adding a probability value corresponding to said added listed item to said item probability distribution if said identified item does not match said any listed item.

717. The processing device of claim 716, wherein another item on said list is replaced with said added listed item, and another probability value corresponding to said replaced listed item is replaced with said added probability value.

718. The processing device of claim 700, wherein said performance index is instantaneous.

719. The processing device of claim 700, wherein said performance index is cumulative.

720. The processing device of claim 700, wherein said probabilistic learning module comprises a learning automaton.

721. The processing device of claim 700, wherein said probabilistic learning module is purely frequency-based.

722. The processing device of claim 700, wherein said learning module and said intuition module are self-contained in a single device.

723. The processing device of claim 700, wherein said learning module and said intuition module are contained in a telephone, and said items are phone numbers.

724. The processing device of claim 700, wherein said learning module and said intuition module are contained in television channel control system, and said items are television channels.

725. The processing device of claim 702, further comprising a favorite item function key the operation of which prompts said item selection module to select said one or more items.

726. The processing device of claim 702, wherein said list is one of a plurality of like lists corresponding to a plurality of users, and said item selection module is further configured for determining which user performed said identified item, wherein said list corresponds with said determined user.

727. The processing device of claim 726, further comprising a plurality of user function keys, wherein said user determination is based on the operation of one of said plurality of user function keys.

728. A method of providing learning capability to a processing device having an objective, comprising:

generating a plurality of lists respectively corresponding to a plurality of item parameter values, each of said plurality of lists containing a plurality of listed items with an associated item probability distribution comprising a plurality of probability values corresponding to said plurality of listed items;

selecting a list corresponding to a parameter value exhibited by a currently identified action associated item; and

in said selected list, selecting one or more listed items from said plurality of listed items based on said item probability distribution;

determining a performance index indicative of a performance of said processing device relative to said objective; and

modifying said item probability distribution based on said performance index.

729. The method of claim 728, further comprising:

identifying an action associated item exhibiting a parameter value;

selecting a list corresponding to said identified parameter value; and

determining if said identified item matches any listed item contained in said selected list, wherein said performance index is based on said matching determination.

730. The method of claim 728, wherein said selected one or more listed items comprises a plurality of selected items.

731. The method of claim 728, wherein said selected one or more listed items comprises a single selected item.

732. The method of claim 728, wherein said selected one or more listed items corresponds to the highest one or more probability values in said item probability distribution.

733. The method of claim 728, further comprising placing said plurality of listed items in an order according to corresponding probability values.

734. The method of claim 728, wherein said item probability distribution is modified by updating said item probability distribution.

735. The method of claim 734, wherein said item probability distribution update comprises a reward-inaction update.

736. The method of claim 728, wherein said item probability distribution is modified by increasing a probability value.

737. The method of claim 728, wherein said item probability distribution is modified by adding a probability value.

738. The method of claim 737, wherein said item probability distribution is modified by replacing a probability value with said added probability value.

739. The method of claim 728, wherein said performance index is instantaneous.

740. The method of claim 728, wherein said performance index is cumulative.

741. The method of claim 728, wherein said item probability distribution is modified by updating it if said identified item matches said any listed item.

742. The method of claim 741, wherein said item probability distribution update comprises a reward-inaction update.

743. The method of claim 742, wherein a corresponding probability value is rewarded if said identified item matches said any listed item.

744. The method of claim 741, further comprising adding a listed item corresponding to said identified item to said selected list if said identified item does not match said any listed item, wherein said item probability distribution is modified by adding a probability value corresponding to said added listed item to said item probability distribution.

745. The method of claim 744, wherein another listed item on said selected list is replaced with said added listed item, and another probability value corresponding to said replaced listed item is replaced with said added probability value.

746. The method of claim 728, wherein said item probability distribution is modified by updating it only if said identified item matches a listed item within said selected one or more items.

747. The method of claim 746, wherein said item probability distribution update comprises a reward-inaction update.

748. The method of claim 747, wherein a corresponding probability value is rewarded if said identified item matches said any listed item.

749. The method of claim 746, wherein said item probability distribution is modified by increasing a corresponding probability value if said identified item matches a listed item that does not correspond to an item in said selected one or more items.

750. The method of claim 746, further comprising adding a listed item corresponding to said identified item to said selected list if said identified item does not match said any listed item, wherein said item probability distribution is modified by adding a probability value corresponding to said added listed item to said item probability distribution.

751. The method of claim 750, wherein another listed item on said selected list is replaced with said added listed item, and another probability value corresponding to said replaced listed item is replaced with said added probability value.

752. The method of claim 728, wherein said processing device comprises a phone, and said item is a phone number.

753. The method of claim 728, wherein said processing device is a television channel control system and said item is a television channel.

754. The method of claim 728, wherein said item probability distribution is updated using a learning automaton.

755. The method of claim 728, wherein said item probability distribution is purely frequency based.

756. The method of claim 755, wherein said item probability distribution is based on a moving average.

757. A method of providing learning capability to a phone number calling system having an objective of anticipating called phone numbers, comprising:

generating a phone list containing at least a plurality of listed phone numbers and a phone number probability distribution comprising a plurality of probability values corresponding to said plurality of listed phone numbers;

selecting a set of phone numbers from said plurality of listed phone numbers based on said phone number probability distribution;

determining a performance index indicative of a performance of said phone number calling system relative to said objective; and

modifying said phone number probability distribution based on said performance index.

758. The method of claim 757, further comprising:

identifying a phone number associated with a phone call; and

determining if said identified phone number matches any listed phone number contained in said phone number list, wherein said performance index is derived from said matching determination.

759. The method of claim 757, wherein said selected phone number set is communicated to a user of said phone number calling system.

760. The method of claim 759, wherein said selected phone number set is displayed to said user.

761. The method of claim 757, wherein said selected phone number set comprises a plurality of selected phone numbers.

762. The method of claim 757, further comprising selecting a phone number from said selected phone number set to make a phone call.

763. The method of claim 757, wherein said selected phone number set corresponds to the highest probability values in said phone number probability distribution.

764. The method of claim 757, further comprising placing said selected phone number set in an order according to corresponding probability values.

765. The method of claim 757, wherein said identified phone number is associated with an outgoing phone call.

766. The method of claim 757, wherein said identified phone number is associated with an incoming phone call.

767. The method of claim 757, wherein said phone number probability distribution is modified by updating said phone number probability distribution.

768. The method of claim 767, wherein said phone number probability distribution update comprises a reward-inaction update.

769. The method of claim 757, wherein said phone number probability distribution is modified by increasing a probability value.

770. The method of claim 757, wherein said phone number probability distribution is modified by adding a probability value.

771. The method of claim 770, wherein said phone number probability distribution is modified by replacing a probability value with said added probability value.

772. The method of claim 757, wherein said plurality of probability values correspond to all phone numbers within said phone number list.

773. The method of claim 757, wherein said plurality of probability values correspond only to said plurality of phone numbers.

774. The method of claim 757, wherein said performance index is instantaneous.

775. The method of claim 757, wherein said performance index is cumulative.

776. The method of claim 757, wherein said phone number probability distribution is normalized.

777. The method of claim 758, wherein said phone number probability distribution is modified by updating it if said identified phone number matches said any listed phone number.

778. The method of claim 777, wherein said phone number probability distribution update comprises a reward-inaction update.

779. The method of claim 778, wherein a corresponding probability value is rewarded if said identified phone number matches said any listed phone number.

780. The method of claim 777, further comprising adding a listed phone number corresponding to said identified phone number to said phone list if said identified phone number does not match said any listed phone number, wherein said phone number probability distribution is modified by adding a probability value corresponding to said added listed phone number to said phone number probability distribution.

781. The method of claim 780, wherein another phone number on said phone list is replaced with said added listed phone number, and another probability value corresponding to said replaced listed phone number is replaced with said added probability value.

782. The method of claim 758, wherein said phone number probability distribution is modified by updating it only if said identified phone number matches a phone number within said selected phone number set.

783. The method of claim 782, wherein said phone number probability distribution update comprises a reward-inaction update.

784. The method of claim 783, wherein a corresponding probability value is rewarded if said identified phone number matches said any listed phone number.

785. The method of claim 782, wherein said phone number probability distribution is modified by increasing a corresponding probability value if said identified phone number matches a listed phone number that does not correspond to a phone number in said selected phone number set.

786. The method of claim 782, further comprising adding a listed phone number corresponding to said identified phone number to said phone list if said identified phone number does not match said any listed phone number, wherein said phone number probability distribution is modified by adding a probability value corresponding to said added listed phone number to said phone number probability distribution.

787. The method of claim 786, wherein another phone number on said phone list is replaced with said added listed phone number, and another probability value corresponding to said replaced listed phone number is replaced with said added probability value.

788. The method of claim 757, wherein said phone number calling system comprises a phone.

789. The method of claim 757, wherein said phone number calling system comprises a mobile phone.

790. The method of claim 757, further comprising:

generating another phone list containing at least another plurality of listed phone numbers and a phone number probability distribution comprising a plurality of probability values corresponding to said other plurality of listed phone numbers; and

selecting another set of phone numbers from said other plurality of phone numbers based on said other phone number probability distribution.

791. The method of claim 790, further comprising:

identifying a phone number associated with a phone call; and

determining if said identified phone number matches any listed phone number contained in said phone number list;

identifying another phone number associated with another phone call; and

determining if said other identified phone number matches any listed phone number contained in said other phone number list;

wherein said performance index is derived from said matching determinations.

792. The method of claim 790, further comprising:

identifying a phone number associated with a phone call;

determining the current day of the week;

selecting one of said phone list and said other phone list based on said current day determination; and

determining if said identified phone number matches any listed phone number contained in said selected phone number list, wherein said performance index is derived from said determination.

793. The method of claim 790, further comprising:

identifying a phone number associated with a phone call;

determining a current time of the day;

selecting one of said phone list and said other phone list based on said current time determination; and

determining if said identified phone number matches any listed phone number contained in said selected phone number list, wherein said performance index is derived from said matching determination.

794. The method of claim 757, wherein said phone number probability distribution is updated using a learning automaton.

795. The method of claim 757, wherein said phone number probability distribution is purely frequency based.

796. The method of claim 795, wherein said phone number probability distribution is based on a moving average.

797. A phone number calling system having an objective of anticipating called phone numbers, comprising:

a probabilistic learning module configured for learning favorite phone numbers of a user in response to phone calls; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said objective.

798. The phone number calling system of claim 797, wherein said probabilistic learning module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and said intuition module is configured for modifying said probabilistic learning module functionality based on said performance index.

799. The phone number calling system of claim 797, further comprising a display for displaying said favorite phone numbers.

800. The phone number calling system of claim 797, further comprising one or more selection buttons configured for selecting one of said favorite phone numbers to make a phone call.

801. The phone number calling system of claim 797, wherein said phone calls are outgoing phone calls.

802. The phone number calling system of claim 797, wherein said phone calls are incoming phone calls.

803. The phone number calling system of claim 797, wherein said probabilistic learning module comprises:

a phone number selection module configured for selecting said favorite phone numbers from a plurality of phone numbers based on a phone number probability distribution comprising a plurality of probability values corresponding to said plurality of listed phone numbers;

an outcome evaluation module configured for determining if an identified phone number associated with a phone call matches any listed phone number contained in said phone number list; and

a probability update module, wherein said intuition module is configured for modifying said probability update module based on said matching determinations.

804. The phone number calling system of claim 803, wherein said favorite phone numbers correspond to the highest probability values in said phone number probability distribution.

805. The phone number calling system of claim 803, wherein said phone number selection module is further configured for placing said favorite numbers in an order according to corresponding probability values.

806. The phone number calling system of claim 803, wherein said intuition module is configured for modifying said probability update module by directing it to update said phone number probability distribution if said identified phone number matches said any listed phone number.

807. The phone number calling system of claim 806, wherein said probability update module is configured for updating said phone number probability distribution using a reward-inaction algorithm.

808. The phone number calling system of claim 807, wherein said probability update module is configured for rewarding a corresponding probability value.

809. The phone number calling system of claim 806, wherein said intuition module is configured for modifying said probability update module by adding a listed phone number corresponding to said identified phone number to said phone list and adding a probability value corresponding to said added listed phone number to said phone number probability distribution if said identified phone number does not match said any listed phone number.

810. The phone number calling system of claim 809, wherein another phone number on said phone list is replaced with said added listed phone number, and another probability value corresponding to said replaced listed phone number is replaced with said added probability value.

811. The phone number calling system of claim 803, wherein said intuition module is configured for modifying said probability update module by directing it to update said phone number probability distribution only if said identified phone number matches a listed phone number corresponding to one of said favorite phone numbers.

812. The phone number calling system of claim 811, wherein said probability update module is configured for updating said phone number probability distribution using a reward-inaction algorithm.

813. The phone number calling system of claim 812, wherein said probability update module is configured for rewarding a corresponding probability value.

814. The phone number calling system of claim 811, wherein said intuition module is configured for modifying said probability update module by increasing a corresponding probability value if said identified phone number matches a listed phone number that does not correspond to one of said favorite phone numbers.

815. The phone number calling system of claim 811, wherein said intuition module is configured for modifying said probability update module by adding a listed phone number corresponding to said identified phone number to said phone list and adding a probability value corresponding to said added listed phone number to said phone number probability distribution if said identified phone number does not match said any listed phone number.

816. The phone number calling system of claim 815, wherein another phone number on said phone list is replaced with said added listed phone number, and another probability value corresponding to said replaced listed phone number is replaced with said added probability value.

817. The phone number calling system of claim 803, wherein said plurality of probability values correspond to all phone numbers within said phone number list.

818. The phone number calling system of claim 803, wherein said plurality of probability values correspond only to said plurality of listed phone numbers.

819. The phone number calling system of claim 798, wherein said performance index is instantaneous.

820. The phone number calling system of claim 798, wherein said performance index is cumulative.

821. The phone number calling system of claim 797, wherein said favorite phone numbers are divided into first and second favorite phone number lists, and said probabilistic learning module is configured for learning said first favorite phone number list in response to phone calls during a first time period, and for learning said second favorite phone number list in response to phone calls during a second time period.

822. The phone number calling system of claim 821, wherein said first time period includes weekdays, and said second time period includes weekends.

823. The phone number calling system of claim 821, wherein said first time period includes days, and said second time period includes evenings.

824. The phone number calling system of claim 797, wherein said probabilistic learning module comprises a learning automaton.

825. The phone number calling system of claim 797, wherein said probabilistic learning module is purely frequency-based.

826. The phone number calling system of claim 797, wherein said learning module and said intuition module are self-contained in a single device.

827. The phone number calling system of claim 797, wherein said learning module and said intuition module are contained in a telephone.

828. The phone number calling system of claim 827, wherein said telephone is a mobile telephone.

829. The phone number calling system of claim 797, wherein said learning module and said intuition module are contained in a server.

830. The phone number calling system of claim 797, wherein said learning module and said intuition module are distributed within a server and a phone.

831. A method of providing learning capability to a phone number calling system, comprising:

identifying a plurality of phone numbers associated with a plurality of phone calls;

maintaining a phone list containing said plurality of phone numbers and a plurality of priority values respectively associated with said plurality of phone numbers;

selecting a set of phone numbers from said plurality of listed phone numbers based on said plurality of priority values;

communicating said phone number set to a user.

832. The method of claim 831, further comprising updating a phone number probability distribution containing said plurality of priority values using a learning automaton.

833. The method of claim 831, further comprising updating a phone number probability distribution containing said plurality of priority values based purely on the frequency of said plurality of phone numbers.

834. The method of claim 833, wherein each of said plurality of priority values is based on a total number of times said associated phone number is identified during a specified time period.

835. The method of claim 831, wherein said selected phone number set is displayed to said user.

836. The method of claim 831, wherein said selected phone number set comprises a plurality of selected phone numbers.

837. The method of claim 831, further comprising selecting a phone number from said selected phone number set to make a phone call.

838. The method of claim 831, wherein said selected phone number set corresponds to the highest priority values.

839. The method of claim 831, further comprising placing said selected phone number set in an order according to corresponding priority values.

840. The method of claim 831, wherein said plurality of phone numbers is associated with outgoing phone calls.

841. The method of claim 831, wherein said plurality of phone numbers is associated with incoming phone calls.

842. The method of claim 831, wherein said phone number calling system comprises a phone.

843. The method of claim 831, wherein said phone number calling system comprises a mobile phone.

844. A method of providing learning capability to a television channel control system having an objective of anticipating watched television channels, comprising:

generating a list containing a plurality of listed television channels with an associated television channel probability distribution comprising a plurality of probability values corresponding to said plurality of listed television channels;

selecting one or more television channels from said plurality of listed television channels based on said television channel probability distribution;

determining a performance index indicative of a performance of said processing device relative to said objective; and

modifying said television channel probability distribution based on said performance index.

845. The method of claim 844, wherein said one or more television channels is selected in response to an operation of a function key.

846. The method of claim 844, further comprising:

identifying a watched television channel; and

determining if said identified television channel matches any listed television channel contained in said television channel list, wherein said performance index is derived from said matching determination.

847. The method of claim 844, wherein said selected one or more television channels comprises a plurality of selected television channels.

848. The method of claim 844, wherein said selected one or more television channels comprises a single selected television channel.

849. The method of claim 848, further comprising tuning said television to a television channel corresponding to said selected single television channel.

850. The method of claim 844, wherein said selected one or more television channels corresponds to the highest one or more probability values in said television channel probability distribution.

851. The method of claim 844, further comprising placing said selected one or more television channels in an order according to corresponding probability values.

852. The method of claim 844, wherein said television channel probability distribution is modified by updating said television channel probability distribution.

853. The method of claim 852, wherein said television channel probability distribution update comprises a reward-inaction update.

854. The method of claim 844, wherein said television channel probability distribution is modified by increasing a probability value.

855. The method of claim 844, wherein said television channel probability distribution is modified by adding a probability value.

856. The method of claim 855, wherein said television channel probability distribution is modified by replacing a probability value with said added probability value.

857. The method of claim 844, wherein said performance index is instantaneous.

858. The method of claim 844, wherein said performance index is cumulative.

859. The method of claim 844, wherein said television channel probability distribution is normalized.

860. The method of claim 846, wherein said television channel probability distribution is modified by updating it if said identified television channel matches said any listed television channel.

861. The method of claim 860, wherein said television channel probability distribution update comprises a reward-inaction update.

862. The method of claim 861, wherein a corresponding probability value is rewarded if said identified television channel matches said any listed television channel.

863. The method of claim 860, further comprising adding a listed television channel corresponding to said identified television channel to said television channel list if said identified television channel does not match said any listed television channel, wherein said television channel probability distribution is modified by adding a probability value corresponding to said added listed television channel to said television channel probability distribution.

864. The method of claim 863, wherein another television channel on said television channel list is replaced with said added listed television channel, and another probability value corresponding to said replaced listed television channel is replaced with said added probability value.

865. The method of claim 846, wherein said television channel probability distribution is modified by updating it only if said identified television channel matches a television channel within said selected one or more television channels.

866. The method of claim 865, wherein said television channel probability distribution update comprises a reward-inaction update.

867. The method of claim 866, wherein a corresponding probability value is rewarded if said identified television channel matches said any listed television channel.

868. The method of claim 865, wherein said television channel probability distribution is modified by increasing a corresponding probability value if said identified television channel matches a listed television channel that does not correspond to a television channel in said selected one or more television channels.

869. The method of claim 865, further comprising adding a listed television channel corresponding to said identified television channel to said television channel list if said identified television channel does not match said any listed television channel, wherein said television channel probability distribution is modified by adding a probability value corresponding to said added listed television channel to said television channel probability distribution.

870. The method of claim 869, wherein another television channel on said television channel list is replaced with said added listed television channel, and another probability value corresponding to said replaced listed television channel is replaced with said added probability value.

871. The method of claim 844, further comprising:

generating another television channel list containing at least another plurality of listed television channels and a television channel probability distribution comprising a plurality of probability values corresponding to said other plurality of listed television channels; and

selecting another one or more television channels from said other plurality of television channels based on said other television channel probability distribution.

872. The method of claim 871, further comprising:

identifying a watched television channel;

determining if said identified television channel matches any listed television channel contained in said television channel list;

identifying another watched television channel; and

determining if said other identified television channel matches any listed television channel contained in said other television channel list;

wherein said performance index is derived from said matching determinations.

873. The method of claim 871, further comprising:

identifying a watched television channel;

determining current temporal information;

selecting one of said television channel list and said other television channel list based on said current temporal information determination; and

determining if said identified television channel matches any listed television channel contained in said selected television channel list, wherein said performance index is derived from said determination.

874. The method of claim 873, wherein said temporal information is a day of the week.

875. The method of claim 873, wherein said temporal information is a time of the day.

876. The method of claim 844, wherein said television channel probability distribution is updated using a learning automaton.

877. The method of claim 844, wherein said television channel probability distribution is purely frequency based.

878. The method of claim 877, wherein said television channel probability distribution is based on a moving average.

879. The method of claim 846, wherein said television channel list is one of a plurality of like television channel lists corresponding to a plurality of users, the method further comprising determining which user watched said identified television channel, wherein said list corresponds with said determined user.

880. The method of claim 879, wherein said user determination is based on the operation of one of a plurality of keys associated with said television channel control system.

881. A television channel control system having an objective of anticipating watched television channels, comprising:

a probabilistic learning module configured for learning favorite television channels of a user in response to watched television channels by said user; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said objective.

882. The television channel control system of claim 881, wherein said probabilistic learning module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and said intuition module is configured for modifying said probabilistic learning module functionality based on said performance index.

883. The television channel control system of claim 881, wherein said probabilistic learning module comprises:

a television channel selection module configured for selecting said one or more of said plurality of favorite television channels from a plurality of television channels contained in a television channel list, said selection being based on a television channel probability distribution comprising a plurality of probability values corresponding to said plurality of listed television channels;

an outcome evaluation module configured for determining if an identified watched television channel matches any listed television channel contained in said television channel list; and

a probability update module, wherein said intuition module is configured for modifying said probability update module based on said matching determination.

884. The television channel control system of claim 883, wherein said one or more favorite parameter values comprises a plurality of favorite parameter values.

885. The television channel control system of claim 883, wherein said one or more favorite parameter values comprises a single favorite parameter value.

886. The television channel control system of claim 883, wherein said one or more favorite television channels correspond to one or more of the highest probability values in said television channel probability distribution.

887. The television channel control system of claim 883, wherein said television channel selection module is further configured for placing said one or more favorite television channels in an order according to corresponding probability values.

888. The television channel control system of claim 883, wherein said intuition module is configured for modifying said probability update module by directing it to update said television channel probability distribution if said identified television channel matches said any listed television channel.

889. The television channel control system of claim 888, wherein said probability update module is configured for updating said television channel probability distribution using a reward- inaction algorithm.

890. The television channel control system of claim 889, wherein said probability update module is configured for rewarding a corresponding probability value.

891. The television channel control system of claim 888, wherein said intuition module is configured for modifying said probability update module by adding a listed television channel corresponding to said identified television channel to said television channel list and adding a probability value corresponding to said added listed television channels to said television channel probability distribution if said identified television channel does not match said any listed television channel.

892. The television channel control system of claim 891, wherein another television channel on said television channel list is replaced with said added listed television channel, and another probability value corresponding to said replaced listed television channel is replaced with said added probability value.

893. The television channel control system of claim 883, wherein said intuition module is configured for modifying said probability update module by directing it to update said television channel probability distribution only if said identified television channel matches a listed television channel corresponding to one of said favorite television channels.

894. The television channel control system of claim 893, wherein said probability update module is configured for updating said television channel probability distribution using a reward- inaction algorithm.

895. The television channel control system of claim 894, wherein said probability update module is configured for rewarding a corresponding probability value.

896. The television channel control system of claim 893, wherein said intuition module is configured for modifying said probability update module by increasing a corresponding probability value if said identified television channel matches a listed television channel that does not correspond to one of said one or more favorite television channels.

897. The television channel control system of claim 893, wherein said intuition module is configured for modifying said probability update module by adding a listed television channel corresponding to said identified television channel to said television channel list and adding a probability value corresponding to said added listed television channel to said television channel probability distribution if said identified television channel does not match said any listed television channel.

898. The television channel control system of claim 897, wherein another television channel on said television channel list is replaced with said added listed television channel, and another probability value corresponding to said replaced listed television channel is replaced with said added probability value.

899. The television channel control system of claim 881, wherein said performance index is instantaneous.

900. The television channel control system of claim 881, wherein said performance index is cumulative.

901. The television channel control system of claim 881, wherein said probabilistic learning module comprises a learning automaton.

902. The television channel control system of claim 881, wherein said probabilistic learning module is purely frequency-based.

903. The television channel control system of claim 881, wherein said television channel control system is a remote control unit.

904. The television channel control system of claim 883, further comprising a favorite channel function key the operation of which prompts said television channel selection module to select said one or more television channels.

905. The television channel control system of claim 883, wherein said television channel list is one of a plurality of like television channel lists corresponding to a plurality of users, and said television channel selection module is further configured for determining which user watched said identified television channel, wherein said list corresponds with said determined user.

906. The television channel control system of claim 905, further comprising a plurality of user function keys, wherein said user determination is based on the operation of one of said plurality of user function keys.

907. A method of providing learning capability to a television channel control system, comprising:

generating a plurality of lists respectively associated with a plurality of television channel parameter values, each of said plurality of lists containing a plurality of listed television channels with an associated television channel probability distribution comprising a plurality of probability values corresponding to said plurality of listed television channels;

selecting a list corresponding to a television channel parameter value exhibited by a currently watched television channel; and

in said selected list, selecting one or more listed actions from said plurality of listed actions based on said action probability distribution;

determining a performance index indicative of a performance of said processing device relative to said objective; and

modifying said action probability distribution based on said performance index.

908. The method of claim 907, further comprising:

identifying a watched television channel exhibiting a television channel parameter value;

selecting a list corresponding to said television channel parameter;

determining if said identified watched television channel matches any listed television channel contained in said selected list, wherein said performance index is based on said matching determination.

909. The method of claim 908, wherein said selected one or more television channels comprises a plurality of selected television channels.

910. The method of claim 908, wherein said selected one or more television channels comprises a single selected television channel.

911. The method of claim 908, wherein said selected one or more television channels corresponds to the highest one or more probability values in said television channel probability distribution.

912. The method of claim 907, further comprising placing said plurality of listed television channels in an order according to corresponding probability values.

913. The method of claim 907, wherein said television channel probability distribution is modified by updating said television channel probability distribution.

914. The method of claim 913, wherein said television channel probability distribution update comprises a reward-inaction update.

915. The method of claim 907, wherein said television channel probability distribution is modified by increasing a probability value.

916. The method of claim 907, wherein said television channel probability distribution is modified by adding a probability value.

917. The method of claim 916, wherein said television channel probability distribution is modified by replacing a probability value with said added probability value.

918. The method of claim 907, wherein said performance index is instantaneous.

919. The method of claim 907, wherein said performance index is cumulative.

920. The method of claim 907, wherein said television channel probability distribution is modified by updating it if said identified television channel matches said any listed television channel.

921. The method of claim 920, wherein said television channel probability distribution update comprises a reward-inaction update.

922. The method of claim 921, wherein a corresponding probability value is rewarded if said identified television channel matches said any listed television channel.

923. The method of claim 920, further comprising adding a listed television channel corresponding to said identified television channel to said television channel list if said identified television channel does not match said any listed television channel, wherein said television channel probability distribution is modified by adding a probability value corresponding to said added listed television channel to said television channel probability distribution.

924. The method of claim 923, wherein another television channel on said television channel list is replaced with said added listed television channel, and another probability value corresponding to said replaced listed television channel is replaced with said added probability value.

925. The method of claim 907, wherein said television channel probability distribution is modified by updating it only if said identified television channel matches a television channel within said selected one or more television channels.

926. The method of claim 925, wherein said television channel probability distribution update comprises a reward-inaction update.

927. The method of claim 926, wherein a corresponding probability value is rewarded if said identified television channel matches said any listed television channel.

928. The method of claim 925, wherein said television channel probability distribution is modified by increasing a corresponding probability value if said identified television channel matches a listed television channel that does not correspond to a television channel in said selected one or more television channels.

929. The method of claim 925, further comprising adding a listed television channel corresponding to said identified television channel to said television channel list if said identified television channel does not match said any listed television channel, wherein said television channel probability distribution is modified by adding a probability value corresponding to said added listed television channel to said television channel probability distribution.

930. The method of claim 929, wherein another television channel on said television channel list is replaced with said added listed television channel, and another probability value corresponding to said replaced listed television channel is replaced with said added probability value.

931. The method of claim 907, wherein said television channel probability distribution is updated using a learning automaton.

932. The method of claim 907, wherein said television channel probability distribution is purely frequency based.

933. The method of claim 907, wherein said television channel probability distribution is based on a moving average.

934. The method of claim 907, wherein said television channel parameter comprises a switched channel number.

935. The method of claim 907, wherein said television channel parameter comprises a channel type.

936. The method of claim 907, wherein said television channel parameter comprises a channel age/gender.

937. The method of claim 907, wherein said television channel parameter comprises a channel rating.

938. A method of providing learning capability to an educational toy, comprising:

selecting one of a plurality of toy actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of toy actions, said plurality of toy actions being associated with a plurality of different difficulty levels;

identifying an action performed by a user;

determining an outcome of said selected toy action relative to said identified user action; and

updating said action probability distribution based on said outcome and said difficulty level of said selected toy action.

939. The method of claim 938, wherein said plurality of toy actions comprises a plurality of educational games.

940. The method of claim 938, wherein said plurality of toy actions comprises a plurality of educational tasks to be performed by said user.

941. The method of claim 940, wherein each of said plurality of educational tasks comprises identifying a combination of items.

942. The method of claim 938, wherein said outcome can be represented by one of two possible values.

943. The method of claim 942, wherein said two possible values are the integers “

zero” and

“

one.”

944. The method of claim 938, wherein said outcome can be represented by one of a finite range of real numbers.

945. The method of claim 938, wherein said outcome can be represented by one of a range of continuous values.

946. The method of claim 938, wherein said identified user action is performed in response to said selected toy action.

947. The method of claim 938, wherein said outcome is determined by determining if said identified user action matches said selected toy action.

948. The method of claim 938, wherein said action probability distribution is shifted from one or more probability values corresponding to one or more toy actions associated with lesser difficulty levels to one or more probability values corresponding to one or more toy actions associated with greater difficulty levels if said outcome indicates that said identified user action is successful relative to said selected toy action.

949. The method of claim 948, wherein said one or more toy actions associated with greater difficulty levels includes a toy action associated with a difficulty level equal to or greater than said difficulty level of said selected toy action.

950. The method of claim 948, wherein said one or more toy actions associated with lesser difficulty levels includes a toy action associated with a difficulty level equal to or less than said difficulty level of said selected toy action.

951. The method of claim 938, wherein said action probability distribution is shifted from one or more probability values corresponding to one or more toy actions associated with greater difficulty levels to one or more probability values corresponding to one or more toy actions associated with lesser difficulty levels if said outcome indicates that said identified user action is unsuccessful relative to said selected toy action.

952. The method of claim 951, wherein said one or more toy actions associated with lesser difficulty levels includes a toy action associated with a difficulty level equal to or less than said difficulty level of said selected toy action.

953. The method of claim 951, wherein said one or more toy actions associated with greater difficulty levels includes a toy action associated with a difficulty level equal to or greater than said difficulty level of said selected toy action.

954. The method of claim 938, wherein said action probability distribution is normalized.

955. The method of claim 938, wherein said selected toy action corresponds to a pseudo-random selection of a probability value within said action probability distribution.

956. The method of claim 938, wherein said action probability distribution is updated using a learning automaton.

957. A method of providing learning capability to an educational toy having an objective of increasing an educational level of a user, comprising:

selecting one of a plurality of toy actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of toy actions, said plurality of toy actions being associated with a plurality of different difficulty levels;

identifying an action performed by said user;

determining an outcome of said selected toy action relative to said identified user action;

updating said action probability distribution based on said outcome; and

modifying one or more of said toy action selection, outcome determination, and action probability distribution update based on said objective.

958. The method of claim 957, wherein said outcome is determined by determining if said identified user action matches said selected toy action.

959. The method of claim 957, further comprising determining a performance index indicative of a performance of said educational toy relative to said objective, wherein said modification is based on said performance index.

960. The method of claim 959, wherein said performance index is derived from said outcome and said difficulty level of said selected toy action.

961. The method of claim 959, wherein said performance index is cumulative.

962. The method of claim 959, wherein said performance index is instantaneous.

963. The method of claim 957, wherein said modification comprises modifying said action probability distribution update.

964. The method of claim 963, wherein said modification comprises selecting one of a predetermined plurality of learning methodologies employed by said action probability distribution update.

965. The method of claim 964, wherein a learning methodology that rewards a toy action having a difficulty level equal to or greater than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is successful relative to said selected toy action.

966. The method of claim 964, wherein a learning methodology that penalizes a toy action having a difficulty level equal to or less than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is successful relative to said selected toy action.

967. The method of claim 964, wherein a learning methodology that rewards a toy action having a difficulty level equal to or less than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected toy action.

968. The method of claim 964, wherein a learning methodology that penalizes a toy action having a difficulty level equal to or greater than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected toy action.

969. The method of claim 957, wherein said action probability distribution is normalized.

970. The method of claim 957, wherein said selected toy action corresponds to a pseudo-random selection of a probability value within said action probability distribution.

971. The method of claim 957, wherein said action probability distribution is updated using a learning automaton.

972. An educational toy having an objective of increasing an educational level of a user, comprising:

a probabilistic learning module configured for learning a plurality of toy actions in response to a plurality of actions performed by a user; and

an intuition module configured for modifying a functionality of said probabilistic learning module based on said objective.

973. The educational toy of claim 972, wherein said intuition module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and for modifying said probabilistic learning module functionality based on said performance index.

974. The educational toy of claim 972, wherein said performance index is cumulative.

975. The educational toy of claim 972, wherein said performance index is instantaneous.

976. The educational toy of claim 972, wherein said probabilistic learning module comprises:

an action selection module configured for selecting one of a plurality of toy actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of toy actions, said plurality of toy actions being associated with a plurality of different difficulty levels;

an outcome evaluation module configured for determining an outcome of said selected toy action relative to said user action; and

a probability update module configured for updating said action probability distribution based on said outcome and said difficulty level of said selected toy action.

977. The educational toy of claim 972, wherein said performance index is derived from said outcome and said difficulty level of said selected toy action.

978. The educational toy of claim 972, wherein said intuition module is configured for modifying a functionality of said probability update module based on said objective.

979. The educational toy of claim 978, wherein said intuition module is configured for selecting one of a predetermined plurality of learning methodologies employed by said probability update module.

980. The educational toy of claim 979, wherein said intuition module is configured for selecting a learning methodology that rewards a toy action having a difficulty level equal to or greater than said difficulty level of said selected toy action if said outcome indicates that said identified user action is successful relative to said selected toy action.

981. The educational toy of claim 979, wherein said intuition module is configured for selecting a learning methodology that penalizes a toy action having a difficulty level equal to or less than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is successful relative to said selected toy action.

982. The educational toy of claim 979, wherein said intuition module is configured for selecting a learning methodology that rewards a toy action having a difficulty level equal to or less than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected toy action.

983. The educational toy of claim 979, wherein said intuition module is configured for selecting a learning methodology that penalizes a toy action having a difficulty level equal to or greater than said difficulty level of said selected toy action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected toy action.

984. The educational toy of claim 979, wherein said probability learning module comprises a learning automaton.

985. A method of providing learning capability to a processing device, comprising:

selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions, said plurality of processor actions being associated with a plurality of different difficulty levels;

identifying an action performed by a user;

determining an outcome of said selected processor action relative to said identified user action; and

updating said action probability distribution based on said outcome and said difficulty level of said selected processor action.

986. The method of claim 985, wherein said processing device is an educational device.

987. The method of claim 986, wherein said plurality of processor actions comprises a plurality of educational games.

988. The method of claim 986, wherein said plurality of processor actions comprises a plurality of educational tasks to be performed by said user.

989. The method of claim 988, wherein each of said plurality of educational tasks comprises identifying a combination of items.

990. The method of claim 985, wherein said outcome can be represented by one of two possible values.

991. The method of claim 990, wherein said two possible values are the integers “

zero” and

“

one.”

992. The method of claim 985, wherein said outcome can be represented by one of a finite range of real numbers.

993. The method of claim 985, wherein said outcome can be represented by one of a range of continuous values.

994. The method of claim 985, wherein said identified user action is performed in response to said selected processor action.

995. The method of claim 985, wherein said outcome is determined by determining if said identified user action matches said action.

996. The method of claim 985, wherein said action probability distribution is shifted from one or more probability values corresponding to one or more actions associated with lesser difficulty levels to one or more probability values corresponding to one or more actions associated with greater difficulty levels if said outcome indicates that said identified user action is successful relative to said selected processor action.

997. The method of claim 996, wherein said one or more actions associated with greater difficulty levels includes a action associated with a difficulty level greater than said difficulty level of said action.

998. The method of claim 996, wherein said one or more actions associated with lesser difficulty levels includes a action associated with a difficulty level less than said difficulty level of said action.

999. The method of claim 985, wherein said action probability distribution is shifted from one or more probability values corresponding to one or more actions associated with greater difficulty levels to one or more probability values corresponding to one or more actions associated with lesser difficulty levels if said outcome indicates that said identified user action is unsuccessful relative to said selected processor action.

1000. The method of claim 999, wherein said one or more actions associated with lesser difficulty levels includes a action associated with a difficulty level less than said difficulty level of said action.

1001. The method of claim 999, wherein said one or more actions associated with greater difficulty levels includes a action associated with a difficulty level greater than said difficulty level of said action.

1002. The method of claim 985, wherein said action probability distribution is normalized.

1003. The method of claim 985, wherein said selected processor action corresponds to a pseudo-random selection of a probability value within said action probability distribution.

1004. The method of claim 985, wherein said action probability distribution is updated using a learning automaton.

1005. A method of providing learning capability to a processing device having one or more objectives, comprising:

selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions, said plurality of processor actions being associated with a plurality of different difficulty levels;

identifying an action performed by said user;

determining an outcome of said selected processor action relative to said identified user action;

updating said action probability distribution based on said outcome; and

modifying one or more of said action selection, outcome determination, and action probability distribution update based on said objective.

1006. The method of claim 1005, wherein said processing device is an educational device, and said one or more objectives comprises increasing the educational level of said user.

1007. The method of claim 1005, wherein said outcome is determined by determining if said identified user action matches said action.

1008. The method of claim 1005, further comprising determining a performance index indicative of a performance of said processing device relative to said objective, wherein said modification is based on said performance index.

1009. The method of claim 1008, wherein said performance index is derived from said outcome and said difficulty level of said selected processor action.

1010. The method of claim 1008, wherein said performance index is cumulative.

1011. The method of claim 1008, wherein said performance index is instantaneous.

1012. The method of claim 1005, wherein said modification comprises modifying said action probability distribution update.

1013. The method of claim 1012, wherein said modification comprises selecting one of a predetermined plurality of learning methodologies employed by said action probability distribution update.

1014. The method of claim 1013, wherein a learning methodology that rewards a action having a difficulty level equal to or greater than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is successful relative to said selected processor action.

1015. The method of claim 1013, wherein a learning methodology that penalizes a action having a difficulty level equal to or less than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is successful relative to said selected processor action.

1016. The method of claim 1013, wherein a learning methodology that rewards a action having a difficulty level equal to or less than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected processor action.

1017. The method of claim 1013, wherein a learning methodology that penalizes a action having a difficulty level equal to or greater than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected processor action.

1018. The method of claim 1005, wherein said action probability distribution is normalized.

1019. The method of claim 1005, wherein said selected processor action corresponds to a pseudo-random selection of a probability value within said action probability distribution.

1020. The method of claim 1005, wherein said action probability distribution is updated using a learning automaton.

1021. A processing device having one or more objectives, comprising:

an action selection module configured for selecting one of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions, said plurality of processor actions being associated with a plurality of different difficulty levels;

an outcome evaluation module configured for determining an outcome of said selected processor action relative to said user action; and

a probability update module configured for updating said action probability distribution based on said outcome and said difficulty level of said selected processor action.

1022. The processing device of claim 1021, wherein said intuition module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and for modifying said probability update module functionality based on said performance index.

1023. The processing device of claim 1021, further comprising an intuition module configured for modifying a functionality of said probability update module based on said one or more objectives.

1024. The processing device of claim 1023, wherein the processing device is an educational device, and said one or more objectives comprises increasing the education level of said user.

1025. The processing device of claim 1024, wherein said plurality of processor actions comprises a plurality of educational games.

1026. The processing device of claim 1024, wherein said plurality of processor actions comprises a plurality of educational tasks to be performed by said user.

1027. The processing device of claim 1026, wherein each of said plurality of educational tasks comprises identifying a combination of items.

1028. The processing device of claim 1026, wherein said intuition module is further configured for determining a performance index indicative of a performance of said probabilistic learning module relative to said objective, and for modifying said probability update module functionality based on said performance index.

1029. The processing device of claim 1028, wherein said performance index is cumulative.

1030. The processing device of claim 1028, wherein said performance index is instantaneous.

1031. The processing device of claim 1028, wherein said performance index is derived from said outcome and said difficulty level of said selected processor action.

1032. The processing device of claim 1023, wherein said intuition module is configured for selecting one of a predetermined plurality of learning methodologies employed by said probability update module.

1033. The processing device of claim 1032, wherein said intuition module is configured for selecting a learning methodology that rewards a processor action having a difficulty level equal to or greater than said difficulty level of said selected processor action if said outcome indicates that said identified user action is successful relative to said selected processor action.

1034. The processing device of claim 1032, wherein said intuition module is configured for selecting a learning methodology that penalizes a processor action having a difficulty level equal to or less than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is successful relative to said selected processor action.

1035. The processing device of claim 1032, wherein said intuition module is configured for selecting a learning methodology that rewards a processor action having a difficulty level equal to or less than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected processor action.

1036. The processing device of claim 1032, wherein said intuition module is configured for selecting a learning methodology that penalizes a processor action having a difficulty level equal to or greater than said difficulty level of said selected processor action is selected if said outcome indicates that said identified user action is unsuccessful relative to said selected processor action.

1037. The processing device of claim 1021, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.