Processing device with intuitive learning capability

• US 20030158827A1
• Filed: 06/26/2002
• Published: 08/21/2003
• Est. Priority Date: 06/26/2001
• Status: Abandoned Application

• Current Assignee: Intuition Intelligence Incorporated
• Original Assignee: Intuition Intelligence Incorporated
• Inventors: Ansari, Arif M., Shiek Ansari, Yusuf Sulaiman M.
• Application Number:
  US10/185,239
• Publication Number:
  US 20030158827A1
• Time in Patent Office: Days
• US Class Current: 706/12
• 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 13/30 Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
• 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 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 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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, is provided. One of a plurality of computer actions to be performed on the computer-based device is selected. In the case of a computer game, the computer actions can take the form of moves taken by a computer-manipulated object. A user input indicative of a user action, such as a move by a user-manipulated object, is received. An outcome value of the selected computer action is determined based on the user action. For example, in the case of a computer game, an intersection between the computer-manipulated object and the user-manipulated object may generate an outcome value indicative of a failure, whereas the non-intersection therebetween may generate an outcome value indicative of a success. An action probability distribution that includes probability values corresponding to said plurality of computer actions is updated based on the determined outcome value. The next computer action will be selected based on this updated action probability distribution. For example, the probability value of the last computer action taken can be increased if the outcome value represents a success, thereby increasing the chance that such computer action will be selected in the future. In contrast, the probability value of the last computer action taken can be decreased if the outcome value represents a failure, thereby decreasing the chance that such computer action will be selected in the future. In this manner, the computer-based device learns the strategy of the user. This learning is directed to achieve one or more objectives of the processing device. For example, in the case of a computer game, the objective may be to match the skill level of the player with that of the game.

Claims

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

receiving 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 value based on one or both of said user action and said selected processor action;

updating said action probability distribution using a learning automaton based on said outcome value; and

modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

2. The method of claim 1, wherein said outcome value is determined based on said user action.

3. The method of claim 1, wherein said outcome value is determined based on said selected processor action.

4. The method of claim 1, wherein said outcome value is determined based on both said user action and said selected processor action.

5. The method of claim 1, wherein said selected processor action is selected in response to said user action.

6. The method of claim 1, further comprising generating a performance index indicative of a performance of said processing device relative to said one or more objectives, wherein said modification is based on said performance index.

7. The method of claim 1, wherein said performance index is updated when said outcome value is determined.

8. The method of claim 6, wherein said performance index is derived from said outcome value.

9. The method of claim 6, wherein said performance index is derived indirectly from said outcome value.

10. The method of claim 6, wherein said performance index is a function of said action probability distribution.

11. The method of claim 6, wherein said performance index is a cumulative value.

12. The method of claim 6, wherein said performance index is an instantaneous value.

13. The method of claim 1, wherein said modification is performed deterministically.

14. The method of claim 1, wherein said modification is performed quasi-deterministically.

15. The method of claim 1, wherein said modification is performed probabilistically.

16. The method of claim 1, wherein said modification is performed using artificial intelligence.

17. The method of claim 1, wherein said modification is performed using an expert system.

18. The method of claim 1, wherein said modification is performed using a neural network.

19. The method of claim 1, wherein said modification is performed using fuzzy logic.

20. The method of claim 1, wherein said modification comprises modifying a subsequently performed action selection step.

21. The method of claim 1, wherein said modification comprises modifying a subsequently performed outcome value determination step.

22. The method of claim 1, wherein said modification comprises modifying a subsequently performed action probability distribution update step.

23. The method of claim 1, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequently performed processor action selection, outcome value determination, and action probability distribution update steps.

24. The method of claim 1, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequently performed processor action selection, outcome value determination, and action probability distribution update steps.

25. The method of claim 1, wherein said outcome value is selected from only two values.

26. The method of claim 25, wherein said outcome value is selected from the integers “

zero” and

“

one.”

27. The method of claim 1, wherein said outcome value is selected from a finite range of real numbers.

28. The method of claim 1, wherein said outcome value is selected from a range of continuous values.

29. The method of claim 1, wherein said outcome value is determined for said selected processor action.

30. The method of claim 1, wherein said outcome value is determined for a previously selected processor action.

31. The method of claim 1, wherein said outcome value is determined for a subsequently selected processor action.

32. The method of claim 1, further comprising initially generating said action probability distribution with equal probability values.

33. The method of claim 1, further comprising initially generating said action probability distribution with unequal probability values.

34. The method of claim 1, wherein said action probability distribution update comprises a linear update.

35. The method of claim 1, wherein said action probability distribution update comprises a linear reward-penalty update.

36. The method of claim 1, wherein said action probability distribution update comprises a linear reward-inaction update.

37. The method of claim 1, wherein said action probability distribution update comprises a linear inaction-penalty update.

38. The method of claim 1, wherein said action probability distribution update comprises a nonlinear update.

39. The method of claim 1, wherein said action probability distribution update comprises an absolutely expedient update.

40. The method of claim 1, wherein said action probability distribution is normalized.

41. The method of claim 1, wherein said selected processor action corresponds to the highest probability value within said action probability distribution.

42. The method of claim 1, wherein said selected processor action is pseudo-randomly selected from said plurality of processor actions.

43. The method of claim 1, wherein said processing device is a computer game, said user action is a player action, and said processor actions are game actions.

44. The method of claim 1, wherein said processing device is a telephone system, said user action is a called phone number, and said processor actions are listed phone numbers.

62. The processing device of claim 44, wherein said intuition module is configured for modifying a parameter of an algorithm employed by said learning module.

45. 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.

46. The processing device of claim 45, wherein said intuition module is further configured for generating 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.

47. The processing device of claim 45, wherein said intuition module is deterministic.

48. The processing device of claim 45, wherein said intuition module is quasi-deterministic.

49. The processing device of claim 45, wherein said intuition module is probabilistic.

50. The processing device of claim 45, wherein said intuition module comprises artificial intelligence.

51. The processing device of claim 45, wherein said intuition module comprises an expert system.

52. The processing device of claim 45, wherein said intuition module comprises a neural network.

53. The processing device of claim 45, wherein said intuition module comprises fuzzy logic.

54. The processing device of claim 45, 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 value based on one or both of said user action and said selected processor action; and

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

55. The processing device of claim 54, wherein said outcome value is determined based on said user action.

56. The processing device of claim 54, wherein said outcome value is determined based on said selected processor action.

57. The processing device of claim 54, wherein said outcome value is determined based on both said user action and said selected processor action.

58. The processing device of claim 54, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

59. The processing device of claim 54, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

60. The processing device of claim 54, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

61. The processing device of claim 45, wherein said intuition module is configured for selecting one of a predetermined plurality of algorithms employed by said learning module.

63. 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:

receiving an action performed by said game player;

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

determining an outcome value based on said player action and said selected game action;

updating said action probability distribution based on said outcome value; and

modifying one or more subsequent game action selections, outcome value determinations, and action probability distribution updates based on said objective.

64. The method of claim 63, wherein said selected game action is selected in response to said player action.

65. The method of claim 63, further comprising generating a performance index indicative of a performance of said computer game relative to said objective, wherein said modification is based on said performance index.

66. The method of claim 65, wherein said performance index comprises a relative score value between said game player and said computer game.

67. The method of claim 63, wherein said performance index is updated when said outcome value is determined.

68. The method of claim 65, wherein said performance index is derived from said outcome value.

69. The method of claim 65, wherein said performance index is derived indirectly from said outcome value.

70. The method of claim 65, wherein said performance index is a function of said action probability distribution.

71. The method of claim 65, wherein said performance index is a cumulative value.

72. The method of claim 65, wherein said performance index is an instantaneous value.

73. The method of claim 63, wherein said modification is performed deterministically.

74. The method of claim 63, wherein said modification is performed quasi-deterministically.

75. The method of claim 63, wherein said modification is performed probabilistically.

76. The method of claim 63, wherein said modification is performed using artificial intelligence.

77. The method of claim 63, wherein said modification is performed using an expert system.

78. The method of claim 63, wherein said modification is performed using a neural network.

79. The method of claim 63, wherein said modification is performed using fuzzy logic.

80. The method of claim 63, wherein said modification comprises modifying a subsequently performed action selection step.

81. The method of claim 80, wherein said plurality of game actions are organized into a plurality of game action subsets, said selected game action is selected from one of said plurality of game action subsets, and said subsequent action selection comprises selecting another of said plurality of game action subsets.

82. The method of claim 81, wherein said subsequently performed action selection comprises selecting another game action from said another of said plurality of game action subsets in response to another player action.

83. The method of claim 63, wherein said modification comprises modifying a subsequently performed outcome value determination step.

84. The method of claim 63, wherein said modification comprises modifying a subsequently performed action probability distribution update step.

85. The method of claim 63, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequently performed game action selection, outcome value determination, and action probability distribution update steps.

86. The method of claim 63, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequently performed game action selection, outcome value determination, and action probability distribution update steps.

87. The method of claim 63, wherein said outcome value is selected from only two values.

88. The method of claim 87, wherein said outcome value is selected from the integers “

zero” and

“

one.”

89. The method of claim 63, wherein said outcome value is selected from a finite range of real numbers.

90. The method of claim 63, wherein said outcome value is selected from a range of continuous values.

91. The method of claim 63, wherein said outcome value is determined for said selected game action.

92. The method of claim 63, wherein said outcome value is determined for a previously selected game action.

93. The method of claim 63, wherein said outcome value is determined for a subsequently selected game action.

94. The method of claim 63, wherein said outcome value is determined by performing a collision technique on said player action and said selected game action.

95. The method of claim 63, further comprising initially generating said action probability distribution with equal probability values.

96. The method of claim 63, further comprising initially generating said action probability distribution with unequal probability values.

97. The method of claim 63, wherein said action probability distribution update comprises a linear update.

98. The method of claim 63, wherein said action probability distribution update comprises a linear reward-penalty update.

99. The method of claim 63, wherein said action probability distribution update comprises a linear reward-inaction update.

100. The method of claim 63, wherein said action probability distribution update comprises a linear inaction-penalty update.

101. The method of claim 63, wherein said action probability distribution update comprises a nonlinear update.

102. The method of claim 63, wherein said action probability distribution update comprises an absolutely expedient update.

103. The method of claim 63, wherein said action probability distribution is normalized.

104. The method of claim 63, wherein said selected game action corresponds to the highest probability value within said action probability distribution.

105. The method of claim 63, wherein said selected game action is pseudo-randomly selected from said plurality of processor actions.

106. The method of claim 63, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

107. The method of claim 106, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

108. The method of claim 106, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

109. The method of claim 106, wherein said player action comprises a simulated shot taken by said user-manipulated object.

110. The method of claim 106, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

111. The method of claim 63, wherein said action probability distribution is updated using a learning automaton.

112. A computer game having an objective of for 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 actions in response to a plurality of actions performed by a game player; and

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

113. The computer game of claim 112, wherein said intuition module is further configured for generating 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.

114. The computer game of claim 113, wherein said performance index comprises a relative score value between said game player and said computer game.

115. The computer game of claim 112, wherein said intuition module is deterministic.

116. The computer game of claim 112, wherein said intuition module is quasi-deterministic.

117. The computer game of claim 112, wherein said intuition module is probabilistic.

118. The computer game of claim 112, wherein said intuition module comprises artificial intelligence.

119. The computer game of claim 112, wherein said intuition module comprises an expert system.

120. The computer game of claim 112, wherein said intuition module comprises a neural network.

121. The computer game of claim 112, wherein said intuition module comprises fuzzy logic.

122. The computer game of claim 112, wherein said probabilistic learning module comprises:

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

an outcome evaluation module configured for determining an outcome value based on said player action and said selected game action; and

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

123. The computer game of claim 122, wherein said intuition module is configured for modifying a functionality of said action selection module based on said objective.

124. The computer game of claim 122, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said objective.

125. The computer game of claim 122, wherein said intuition module is configured for modifying a functionality of said probability update module based on said objective.

126. The computer game of claim 122, wherein said intuition module is configured for selecting one of a predetermined plurality of algorithms employed by said learning module.

127. The computer game of claim 122, wherein said intuition module is configured for modifying a parameter of an algorithm employed by said learning module.

128. The computer game of claim 122, wherein said plurality of game actions is performed by a game-manipulated object, and said user action is performed by a user-manipulated object.

129. The computer game of claim 128, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

130. The computer game of claim 128, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

131. The computer game of claim 128, wherein said player action comprises a simulated shot taken by said user-manipulated object.

132. The computer game of claim 128, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

133. The computer game of claim 112, wherein said probability learning module comprises a learning automaton.

135. The method of claim 133, wherein said selected processor action is selected in response to said user action.

136. The method of claim 133, further comprising:

receiving an action performed by a user,

determining an outcome value based on said user action and said selected processor action; and

updating said action probability distribution based on said outcome value.

137. The method of claim 133, wherein said processing device has one or more objectives, the method further comprising generating a performance index indicative of a performance of said processing device relative to said one or more objectives, wherein said action subset selection is based on said performance index.

138. The method of claim 133, wherein said selected action subset is selected deterministically.

139. The method of claim 133, wherein said selected action subset is selected quasi-deterministically.

140. The method of claim 133, wherein said selected action subset is selected probabilistically.

141. The method of claim 133, wherein said selected processor action is pseudo-randomly selected from said selected action subset.

142. The method of claim 133, wherein said selected action subset corresponds to a series of probability values within said action probability distribution.

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

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

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

146. The method of claim 133, wherein said selected action subset corresponds to an average of probability values relative to a threshold value.

147. The method of claim 146, wherein said threshold value is a median probability value within said action probability distribution.

148. The method of claim 146, wherein said threshold value is dynamically adjusted.

149. The method of claim 146, wherein said selected action subset corresponds to an average of probability values greater than said threshold value.

150. The method of claim 146, wherein said selected action subset corresponds to an average of probability values less than said threshold value.

151. The method of claim 146, wherein said selected action subset corresponds to an average of probability values substantially equal to said threshold value.

152. The method of claim 133, wherein said action probability distribution is updated using a learning automaton.

134. 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.

153. A method of providing learning capability to a computer game, 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 game actions;

selecting one of said plurality of action subsets; and

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

154. The method of claim 153, wherein said selected game action is selected in response to said player action.

155. The method of claim 153, further comprising:

receiving an action performed by a game player;

determining an outcome value based on said player action and said selected game action; and

updating said action probability distribution based on said outcome value.

156. The method of claim 155, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

157. The method of claim 156, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

158. The method of claim 156, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

159. The method of claim 156, wherein said player action comprises a simulated shot taken by said user-manipulated object.

160. The method of claim 156, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

161. The method of claim 153, wherein said selected action subset is selected deterministically.

162. The method of claim 153, wherein said selected action subset is selected quasi-deterministically.

163. The method of claim 153, wherein said selected action subset is selected probabilistically.

164. The method of claim 153, wherein said selected processor action is pseudo-randomly selected from said selected action subset.

165. The method of claim 153, wherein said selected action subset corresponds to a series of probability values within said action probability distribution.

166. The method of claim 153, wherein said selected action subset corresponds to the highest probability values within said action probability distribution.

167. The method of claim 153, wherein said selected action subset corresponds to the lowest probability values within said action probability distribution.

168. The method of claim 153, wherein said selected action subset corresponds to the middlemost probability values within said action probability distribution.

169. The method of claim 153, wherein said selected action subset corresponds to an average of probability values relative to a threshold level.

170. The method of claim 169, wherein said threshold level is a median probability value within said action probability distribution.

171. The method of claim 169, wherein said threshold level is dynamically adjusted.

172. The method of claim 169, wherein said selected action subset corresponds to an average of probability values greater than said threshold level.

173. The method of claim 169, wherein said selected action subset corresponds to an average of probability values less than said threshold level.

174. The method of claim 169, wherein said selected action subset corresponds to an average of probability values substantially equal to said threshold level.

175. The method of claim 153, wherein said selected action subset is selected based on a skill level of a game player relative to a skill level of said computer game.

176. The method of claim 175, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

177. The method of claim 175, wherein said action subset is selected to correspond to the highest probability values within said action probability distribution if said relative skill level is greater than a threshold level.

178. The method of claim 175, wherein said action subset is selected to correspond to the lowest probability values within said action probability distribution if said relative skill level is less than a threshold level.

179. The method of claim 175, wherein said action subset is selected to correspond to the middlemost probability values within said action probability distribution if said relative skill level is within a threshold range.

180. The method of claim 175, wherein said game action subset is selected to correspond to an average of probability values relative to a threshold level.

181. The method of claim 180, wherein said threshold level is a median probability value within said action probability distribution.

182. The method of claim 180, wherein said threshold level is dynamically adjusted based on said relative skill level.

183. The method of claim 180, wherein said game action subset is selected to correspond to an average of probability values greater than said threshold level if said relative skill level value is greater than a relative skill threshold level.

184. The method of claim 180, wherein said game action subset is selected to correspond to an average of probability values less than said relative skill threshold level.

185. The method of claim 180, wherein said game action subset is selected to correspond to an average of probability values substantially equal to said threshold level.

186. The method of claim 153, wherein said action probability distribution is updated using a learning automaton.

187. 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.

188. The method of claim 187, further comprising:

receiving an action performed by a user;

selecting one of said plurality of processor actions; and

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

189. The method of claim 188, wherein said outcome value is determined based on said user action.

190. The method of claim 188, wherein said outcome value is determined based on said selected processor action.

191. The method of claim 188, wherein said outcome value is determined based on both said user action and said selected processor action.

192. The method of claim 188, wherein said selected processor action is selected in response to said user action.

193. The method of claim 187, wherein said processing device has one or more objectives, the method further comprising generating 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.

194. The method of claim 187, wherein said one or more learning algorithms are modified deterministically.

195. The method of claim 187, wherein said one or more learning algorithms are modified quasi-deterministically.

196. The method of claim 187, wherein said one or more learning algorithms are modified probabilistically.

197. The method of claim 187, wherein said one or more algorithms comprises one or more parameters, and said algorithm modification comprises modifying said one or more parameters.

198. The method of claim 197, wherein said one or more parameters comprises a reward parameter.

199. The method of claim 197, wherein said one or more parameters comprises a penalty parameter.

200. The method of claim 197, wherein said one or more parameters comprises one or more of a reward parameter and penalty parameter.

201. The method of claim 200, wherein said one or more of a reward parameter and penalty parameter are increased.

202. The method of claim 200, wherein said one or more of a reward parameter and penalty parameter are decreased.

203. The method of claim 200, wherein said one or more of a reward parameter and penalty parameter are modified to a negative number.

204. The method of claim 197, wherein said one or more parameters comprises a reward parameter and a penalty parameter.

205. The method of claim 204, wherein said reward parameter and said penalty parameter are both increased.

206. The method of claim 204, wherein said reward parameter and said penalty parameter are both decreased.

207. The method of claim 204, wherein said reward parameter and said penalty parameter are modified to a negative number.

208. The method of claim 187, wherein said one or more algorithms is linear.

209. The method of claim 187, wherein said action probability distribution is updated using a learning automaton.

210. A method of providing learning capability to a computer game, 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 game actions;

modifying said one or more learning algorithms; and

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

211. The method of claim 210, further comprising:

receiving an action performed by a game player;

selecting one of said plurality of game actions; and

determining an outcome value based on one or both of said player action and said selected game action, wherein said action probability distribution update is based on said outcome value.

212. The method of claim 211, wherein said outcome value is determined based on said player action.

213. The method of claim 211, wherein said outcome value is determined based on said selected game action.

214. The method of claim 211, wherein said outcome value is determined based on both said player action and said selected game action.

215. The method of claim 211, wherein said selected game action is selected in response to said player action.

216. The method of claim 210, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

217. The method of claim 216, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

218. The method of claim 216, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

219. The method of claim 216, wherein said player action comprises a simulated shot taken by said user-manipulated object.

220. The method of claim 216, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

221. The method of claim 210, wherein said one or more learning algorithms are modified deterministically.

222. The method of claim 210, wherein said one or more learning algorithms are modified quasi-deterministically.

223. The method of claim 210, wherein said one or more learning algorithms are modified probabilistically.

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

225. The method of claim 224, wherein said one or more parameters are modified in accordance with a function.

226. The method of claim 224, wherein said one or more parameters comprises a reward parameter.

227. The method of claim 224, wherein said one or more parameters comprises a penalty parameter.

228. The method of claim 224, wherein said one or more parameters comprises one or more of a reward parameter and penalty parameter.

229. The method of claim 228, wherein said one or more of a reward parameter and penalty parameter are increased.

230. The method of claim 228, wherein said one or more of a reward parameter and penalty parameter are decreased.

231. The method of claim 228, wherein said one or more of a reward parameter and penalty parameter are modified to a negative number.

232. The method of claim 224, wherein said one or more parameters comprises a reward parameter and a penalty parameter.

233. The method of claim 232, wherein said reward parameter and said penalty parameter are both increased.

234. The method of claim 232, wherein said reward parameter and said penalty parameter are both decreased.

235. The method of claim 232, wherein said reward parameter and said penalty parameter are modified to a negative number.

236. The method of claim 224, 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.

237. 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.

238. The method of claim 224, 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.

239. The method of claim 238, 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 238, 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.

241. The method of claim 238, 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.

242. The method of claim 238, 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.

243. The method of claim 210, wherein said one or more algorithms is linear.

244. The method of claim 210, 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.

245. The method of claim 244, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

246. The method of claim 244, wherein both of said reward parameter and said penalty parameter are increased if said relative skill level is greater than a threshold level.

247. The method of claim 244, wherein both of said reward parameter and said penalty parameter are decreased if said relative skill level is less than a threshold level.

248. The method of claim 244, 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.

249. The method of claim 244, wherein said one or more algorithms is linear.

250. The method of claim 210, wherein said action probability distribution is updated using a learning automaton.

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

receiving an action performed by said game player;

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

determining if said selected game action is successful;

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

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

252. The method of claim 251, wherein said selected game action is selected in response to said player action.

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

254. The method of claim 251, 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.

255. The method of claim 251, wherein said reward algorithm is linear.

256. The method of claim 251, further comprising modifying said reward algorithm based on said successful game action determination.

257. The method of claim 251, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

258. The method of claim 257, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

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

260. The method of claim 257, wherein said player action comprises a simulated shot taken by said user-manipulated object.

261. The method of claim 257, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

262. The method of claim 251, wherein said action probability distribution is updated using a learning automaton.

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

receiving an action performed by said game player;

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

determining if said selected game action is successful;

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

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

264. The method of claim 263, wherein said selected game action is selected in response to said player action.

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

266. The method of claim 263, 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.

267. The method of claim 263, wherein said penalty algorithm is linear.

268. The method of claim 263, further comprising modifying said penalty algorithm based on said successful game action determination.

269. The method of claim 263, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

270. The method of claim 269, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

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

272. The method of claim 269, wherein said player action comprises a simulated shot taken by said user-manipulated object.

273. The method of claim 269, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

274. The method of claim 263, wherein said action probability distribution is updated using a learning automaton.

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

receiving an action performed by said game player;

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

determining if said selected game action is successful;

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

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

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

276. The method of claim 275, wherein said selected game action is selected in response to said player action.

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

278. The method of claim 275, 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.

279. The method of claim 275, wherein said reward algorithm and said penalty algorithm are linear.

280. The method of claim 275, further comprising modifying said reward algorithm and said penalty algorithm based on said successful game action determination.

281. The method of claim 275, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

282. The method of claim 281, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

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

284. The method of claim 281, wherein said player action comprises a simulated shot taken by said user-manipulated object.

285. The method of claim 281, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

286. The method of claim 275, wherein said action probability distribution is updated using a learning automaton.

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

receiving an action performed by said game player;

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

determining if said selected game action 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 value if said selected game action is successful and said relative skill level is relatively high, or if said selected game action is unsuccessful and said relative skill level is relatively low; and

updating said action probability distribution based on said successful outcome value.

288. The method of claim 287, wherein said selected game action is selected in response to said player action.

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

290. The method of claim 287, 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.

291. The method of claim 287, wherein said successful outcome value equals the value “

1.”

292. The method of claim 287, wherein said successful outcome value equals the value “

0.”

293. The method of claim 287, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

294. The method of claim 293, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

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

296. The method of claim 293, wherein said player action comprises a simulated shot taken by said user-manipulated object.

297. The method of claim 293, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

298. The method of claim 287, wherein said action probability distribution is updated using a learning automaton.

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

receiving an action performed by said game player;

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

determining if said selected game action 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 value if said selected game action is unsuccessful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low; and

updating said action probability distribution based on said unsuccessful outcome value.

300. The method of claim 299, wherein said selected game action is selected in response to said player action.

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

302. The method of claim 299, 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.

303. The method of claim 299, wherein said unsuccessful outcome value equals the value “

1.”

304. The method of claim 299, wherein said unsuccessful outcome value equals the value “

0.”

305. The method of claim 299, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

306. The method of claim 305, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

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

308. The method of claim 305, wherein said player action comprises a simulated shot taken by said user-manipulated object.

309. The method of claim 305, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

310. The method of claim 299, wherein said action probability distribution is updated using a learning automaton.

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

receiving an action performed by said game player;

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

determining if said selected game action 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 value if said selected game action is successful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low;

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

updating said action probability distribution based on said successful outcome value and said unsuccessful outcome value.

312. The method of claim 311, wherein said selected game action is selected in response to said player action.

313. The method of claim 311, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

314. The method of claim 311, 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.

315. The method of claim 311, wherein said successful outcome value equals the value “

1”

, and said unsuccessful outcome value equal the value “

0.”

316. The method of claim 311, wherein said successful outcome value equals the value “

0,” and

said unsuccessful outcome value equal the value “

1.”

317. The method of claim 311, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

318. The method of claim 317, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

319. The method of claim 317, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

320. The method of claim 317, wherein said player action comprises a simulated shot taken by said user-manipulated object.

321. The method of claim 317, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

322. The method of claim 311, wherein said action probability distribution is updated using a learning automaton.

323. 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.

324. The method of claim 323, further comprising:

receiving an action performed by a user;

selecting one of said plurality of processor actions;

determining an outcome value based on said user action and said selected processor action; and

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

325. The method of claim 324, wherein said selected user action is selected in response to said user action.

326. The method of claim 323, wherein said processing device has one or more objectives, the method further comprising generating 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.

327. The method of claim 323, wherein said transformation is performed deterministically.

328. The method of claim 323, wherein said transformation is performed modified quasi-deterministically.

329. The method of claim 323, wherein said transformation is performed probabilistically.

330. The method of claim 323, wherein said action probability distribution transformation comprises assigning a value to one or more of said plurality of probability values.

331. The method of claim 323, wherein said action probability distribution transformation comprises switching a higher probability value and a lower probability value.

332. The method of claim 323, wherein said action probability distribution transformation comprises switching a set of highest probability values and a set lowest probability values.

333. The method of claim 323, wherein said action probability distribution is updated using a learning automaton.

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

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

transforming said action probability distribution.

335. The method of claim 334, further comprising:

receiving an action performed by a game player;

selecting one of said plurality of game actions;

determining an outcome value based on said player action and said selected processor action; and

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

336. The method of claim 335, wherein said selected game action is selected in response to said player action.

337. The method of claim 334, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

338. The method of claim 337, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

339. The method of claim 337, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

340. The method of claim 337, wherein said player action comprises a simulated shot taken by said user-manipulated object.

341. The method of claim 337, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

342. The method of claim 334, wherein said transformation is performed deterministically.

343. The method of claim 334, wherein said transformation is performed modified quasi-deterministically.

344. The method of claim 334, wherein said transformation is performed probabilistically.

345. The method of claim 334, wherein said action probability distribution transformation comprises assigning a value to one or more of said plurality of probability values.

346. The method of claim 334, wherein said action probability distribution transformation comprises switching a higher probability value and a lower probability value.

347. The method of claim 334, wherein said action probability distribution transformation comprises switching a set of highest probability values and a set lowest probability values.

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

349. The method of claim 334, wherein said action probability distribution is transformed based on a skill level of a game player relative to a skill level of said computer game.

350. The method of claim 349, wherein said action 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.

351. The method of claim 349, wherein said action 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.

352. The method of claim 349, wherein said action 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.

353. The method of claim 349, wherein said action 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.

354. The method of claim 334, wherein said action probability distribution is updated using a learning automaton.

355. 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.

356. The method of claim 355, further comprising:

receiving an action performed by a user;

selecting one of said plurality of processor actions;

determining an outcome value based on one or more said user action and said selected processor action; and

updating said action probability distribution based on said outcome value.

357. The method of claim 356, wherein said outcome value is determined based on said user action.

358. The method of claim 356, wherein said outcome value is determined based on said selected processor action.

359. The method of claim 356, wherein said outcome value is determined based on both said user action and said selected processor action.

360. The method of claim 356, wherein said selected user action is selected in response to said user action.

361. The method of claim 355, wherein said processing device has one or more objectives, the method further comprising generating 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.

362. The method of claim 355, wherein said one or more probability values are limited to a high value.

363. The method of claim 355, wherein said one or more probability values are limited to a low value.

364. The method of claim 355, wherein said plurality of probability values is limited.

365. The method of claim 355, wherein said action probability distribution is updated using a learning automaton.

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

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

limiting one or more of said plurality of probability values.

367. The method of claim 366, further comprising:

receiving an action performed by a game player;

selecting one of said plurality of game actions;

determining an outcome value based on said player action and said selected processor action; and

updating said action probability distribution based on said outcome value.

368. The method of claim 367, wherein said selected game action is selected in response to said player action.

369. The method of claim 367, wherein said plurality of game actions is performed by a game-manipulated object, and said player action is performed by a user-manipulated object.

370. The method of claim 367, wherein said plurality of game actions comprises discrete movements of said game-manipulated object.

371. The method of claim 367, wherein said plurality of game actions comprises a plurality of delays related to a movement of said game-manipulated object.

372. The method of claim 367, wherein said player action comprises a simulated shot taken by said user-manipulated object.

373. The method of claim 367, wherein said game-manipulated object and said user-manipulated object are visual to said game player.

374. The method of claim 366, wherein said one or more probability values are limited to a high value.

375. The method of claim 366, wherein said one or more probability values are limited to a low value.

376. The method of claim 366, wherein said plurality of probability values is limited.

377. The method of claim 366, 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.

378. The method of claim 377, wherein said relative skill level is obtained from a difference between a game player score and a computer game score.

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

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

receiving 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 value based on one or both of said user action and said selected processor action;

updating said action probability distribution based on said outcome value; and

repeating said foregoing steps, wherein said action probability distribution is prevented from substantially converging to a single probability value.

381. The method of claim 380, wherein said outcome value is determined based on said user action.

382. The method of claim 380, wherein said outcome value is determined based on said selected processor action.

383. The method of claim 380, wherein said outcome value is determined based on both said user action and said selected processor action.

384. The method of claim 380, wherein said selected processor action is selected in response to said user action.

385. The method of claim 380, wherein said outcome value is selected from only two values.

386. The method of claim 385, wherein said outcome value is selected from the integers “

zero” and

“

one.”

387. The method of claim 380, wherein said outcome value is selected from a finite range of real numbers.

388. The method of claim 380, wherein said outcome value is selected from a range of continuous values.

389. The method of claim 380, wherein said outcome value is determined for said selected processor action.

390. The method of claim 380, wherein said outcome value is determined for a previously selected processor action.

391. The method of claim 380, wherein said outcome value is determined for a subsequently selected processor action.

392. The method of claim 380, further comprising initially generating said action probability distribution with equal probability values.

393. The method of claim 380, further comprising initially generating said action probability distribution with unequal probability values.

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

395. The method of claim 380, wherein said action probability distribution update comprises a linear reward-penalty update.

396. The method of claim 380, wherein said action probability distribution update comprises a linear reward-inaction update.

397. The method of claim 380, wherein said action probability distribution update comprises a linear inaction-penalty update.

398. The method of claim 380, wherein said action probability distribution update comprises a nonlinear update.

399. The method of claim 380, wherein said action probability distribution update comprises an absolutely expedient update.

400. The method of claim 380, wherein said action probability distribution is normalized.

401. The method of claim 380, wherein said selected processor action corresponds to the highest probability value within said action probability distribution.

402. The method of claim 380, wherein said selected processor action is pseudo-randomly selected from said plurality of processor actions.

403. The method of claim 380, wherein said processing device is a computer game, said user action is a player action, and said processor actions are game action.

404. The method of claim 380, wherein said processing device is a telephone system, said user action is a called phone number, and said processor actions are listed phone numbers.

405. The method of claim 380, wherein said action probability distribution is updated using a learning automaton.

406. 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.

407. The processing device of claim 406, wherein said intuition module is deterministic.

408. The processing device of claim 406, wherein said intuition module is quasi-deterministic.

409. The processing device of claim 406, wherein said intuition module is probabilistic.

410. The processing device of claim 406, wherein said intuition module comprises artificial intelligence.

411. The processing device of claim 406, wherein said intuition module comprises an expert system.

412. The processing device of claim 406, wherein said intuition module comprises a neural network.

413. The processing device of claim 406, wherein said intuition module comprises fuzzy logic.

414. The processing device of claim 406, 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 value based on one or both of said user action and said selected processor action; and

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

415. The processing device of claim 414, wherein said outcome value is determined based on said user action.

416. The processing device of claim 414, wherein said outcome value is determined based on said selected processor action.

417. The processing device of claim 414, wherein said outcome value is determined based on both said user action and said selected processor action.

418. The processing device of claim 406, wherein said probability learning module is comprises a learning automaton.

419. A method of providing learning capability to an electronic device having a function independent of determining an optimum action, comprising:

receiving an action performed by a user;

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 electronic device function;

determining an outcome value based on said user action and said selected processor action; and

updating said action probability distribution based on said outcome value.

420. The method of claim 419, wherein said selected processor action is selected in response to said user action.

421. The method of claim 419, wherein said outcome value is selected from only two values.

422. The method of claim 421, wherein said outcome value is selected from the integers “

zero” and

“

one.”

423. The method of claim 419, wherein said outcome value is selected from a finite range of real numbers.

424. The method of claim 419, wherein said outcome value is selected from a range of continuous values.

425. The method of claim 419, wherein said outcome value is determined for said selected processor action.

426. The method of claim 419, wherein said outcome value is determined for a previously selected processor action.

427. The method of claim 419, wherein said outcome value is determined for a subsequently selected processor action.

428. The method of claim 419, further comprising initially generating said action probability distribution with equal probability values.

429. The method of claim 419, further comprising initially generating said action probability distribution with unequal probability values.

430. The method of claim 419, wherein said action probability distribution update comprises a linear update.

431. The method of claim 419, wherein said action probability distribution update comprises a linear reward-penalty update.

432. The method of claim 419, wherein said action probability distribution update comprises a linear reward-inaction update.

433. The method of claim 419, wherein said action probability distribution update comprises a linear inaction-penalty update.

434. The method of claim 419, wherein said action probability distribution update comprises a nonlinear update.

435. The method of claim 419, wherein said action probability distribution update comprises an absolutely expedient update.

436. The method of claim 419, wherein said action probability distribution is normalized.

437. The method of claim 419, wherein said selected processor action corresponds to the highest probability value within said action probability distribution.

438. The method of claim 419, wherein said selected processor action is pseudo-randomly selected from said plurality of processor actions.

439. The method of claim 419, wherein said processing device is a computer game, said user action is a player action, and said processor actions are game actions.

440. The method of claim 419, wherein said processing device is a telephone system, said user action is a called phone number, and said processor actions are listed phone numbers.

441. The method of claim 419, wherein said processing device is a consumer electronics device.

442. The method of claim 419, wherein said processing device is a personal digital assistant.

443. The method of claim 419, wherein said processing device is an audio/video device.

444. The method of claim 419, wherein said action probability distribution is updated using a learning automaton.

445. 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 electronic device function;

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

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

446. The processing device of claim 445, wherein said outcome value is determined based on said user action.

447. The processing device of claim 445, wherein said outcome value is determined based on said selected processor action.

448. The processing device of claim 445, wherein said outcome value is determined based on both said user action and said selected processor action.

449. The processing device of claim 445, wherein said processing device is a computer game.

450. The processing device of claim 445, wherein said processing device is a consumer electronics device.

451. The processing device of claim 445, wherein said processing device is a mobile phone.

452. The processing device of claim 445, wherein said processing device is a personal digital assistant.

453. The processing device of claim 445, wherein said processing device is an audio/video device.

454. The processing device of claim 445, wherein said probability learning module comprises a learning automaton.

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

receiving 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 outcome values based on one or both of said 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 outcome values; and

modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

456. The method of claim 455, wherein said one or more outcome values are based on said plurality of user actions.

457. The method of claim 455, wherein said one or more outcome values are based on said selected one or more processor actions.

458. The method of claim 455, wherein said one or more outcome values are based on both said plurality of user actions and said selected one or more processor actions.

459. The method of claim 455, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

460. The method of claim 455, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

461. The method of claim 455, wherein said one or more outcome values comprises a single outcome value corresponding to said plurality of user actions.

462. The method of claim 455, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to said plurality of user actions.

463. The method of claim 455, wherein said action probability distribution is updated when a predetermined period of time has expired.

464. The method of claim 455, wherein said action probability distribution is updated in response to the receipt of each user action.

465. The method of claim 455, wherein said selected processor action is selected in response to said plurality of user actions.

466. The method of claim 455, further comprising generating 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.

467. The method of claim 466, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of user actions.

468. The method of claim 466, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of user actions.

469. The method of claim 455, wherein said modification comprises modifying a subsequently performed action selection.

470. The method of claim 455, wherein said modification comprises modifying a subsequently performed outcome value determination.

471. The method of claim 455, wherein said modification comprises modifying a subsequently performed action probability distribution update.

472. The method of claim 455, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

473. The method of claim 455, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

474. The method of claim 455, wherein outcome value determination is performed only after several iterations of said user action receiving and processor action selection.

475. The method of claim 455, wherein said probability distribution update is performed only after several iterations of said user action receiving and processor action selection.

476. The method of claim 455, wherein said probability distribution update is performed only after several iterations of said user action receiving, processor action selection, and outcome value determination.

477. The method of claim 455, wherein said processing device is a computer game, said user actions are player actions, and said processor actions are game actions.

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

receiving 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 outcome values based on 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 outcome values; and

modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

479. The method of claim 478, wherein each user set comprises a single user.

480. The method of claim 478, wherein each user set comprises a plurality of users.

481. The method of claim 480, wherein said selected one or more processor actions comprises a single processor action corresponding to actions from said plurality of users.

482. The method of claim 480, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to actions from said plurality of users.

483. The method of claim 480, wherein said one or more outcome values comprises a single outcome value corresponding to actions from said plurality of users.

484. The method of claim 480, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to actions from said plurality of users.

485. The method of claim 478, wherein said action probability distribution is updated when a predetermined period of time has expired.

486. The method of claim 478, wherein said action probability distribution is updated in response to the receipt of each user action.

487. The method of claim 478, wherein said selected one or more processor actions is selected in response to said user actions.

488. The method of claim 478, further comprising generating 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.

489. The method of claim 480, further comprising generating 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 plurality of user actions and said modification is based on said single performance index.

490. The method of claim 480, further comprising generating 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 plurality of user actions and said modification is based on said plurality of performance indexes.

491. The method of claim 478, wherein said modification comprises modifying a subsequently performed action selection.

492. The method of claim 478, wherein said modification comprises modifying a subsequently performed outcome value determination.

493. The method of claim 478, wherein said modification comprises modifying a subsequently performed action probability distribution update.

494. The method of claim 478, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

495. The method of claim 478, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

496. The method of claim 478, wherein outcome value determination is performed only after several iterations of said user action receiving and processor action selection.

497. The method of claim 478, wherein said probability distribution update is performed only after several iterations of said user action receiving and processor action selection.

498. The method of claim 478, wherein said probability distribution update is performed only after several iterations of said user action receiving, processor action selection, and outcome value determination.

499. The method of claim 478, wherein said processing device is a computer game, said user actions are player actions, and said processor actions are game actions.

500. The method of claim 478, wherein said processing device is a telephone system, said user actions are called phone numbers, and said processor actions are listed phone numbers.

501. 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 actions from 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.

502. The processing device of claim 501, wherein said intuition module is further configured for generating 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.

503. The processing device of claim 502, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of users.

504. The processing device of claim 502, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of users.

505. The processing device of claim 501, wherein said one or more outcome values comprises a single outcome value corresponding to said plurality of user actions.

506. The processing device of claim 501, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to said plurality of user actions.

507. The processing device of claim 501, wherein said intuition module is configured for selecting one of a predetermined plurality of algorithms employed by said learning module.

508. The processing device of claim 501, wherein said intuition module is configured for modifying a parameter of an algorithm employed by said learning module.

509. The processing device of claim 501, 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 outcome values based on one or both of said 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 outcome values.

510. The processing device of claim 509, wherein said one or more outcome values are based on said plurality of user actions.

511. The processing device of claim 509, wherein said one or more outcome values are based on said selected one or more processor actions.

512. The processing device of claim 509, wherein said one or more outcome values are based on both said plurality of user actions and said selected one or more processor actions.

513. The processing device of claim 509, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

514. The processing device of claim 509, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

515. The processing device of claim 509, 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.

516. The processing device of claim 509, 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.

517. The processing device of claim 509, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

518. The processing device of claim 509, further comprising:

a server storing 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 plurality of user actions; and

a network configured for transmitting said 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.

519. The processing device of claim 509, 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 storing said one or more outcome evaluation modules, and said probability update module;

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

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

520. The processing device of claim 509, 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 outcome values comprises a plurality of outcome values, the processing device further comprising:

a server storing said probability update module;

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

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

521. The processing device of claim 501, 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 outcome values based on 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 outcome values.

522. The processing device of claim 521, wherein said one or more outcome values are based on said plurality of user actions.

523. The processing device of claim 521, wherein said one or more outcome values are based on said selected one or more processor actions.

524. The processing device of claim 521, wherein said one or more outcome values are based on both said plurality of user actions and said selected one or more processor actions.

525. The processing device of claim 521, wherein each user set comprises a single user.

526. The processing device of claim 521, wherein each user set comprises a plurality of users.

527. The processing device of claim 521, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

528. The processing device of claim 521, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

529. The processing device of claim 521, 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.

530. The processing device of claim 521, 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.

531. The processing device of claim 521, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

532. The processing device of claim 521, further comprising:

a server storing 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 plurality of user actions; and

a network configured for transmitting said 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.

533. The processing device of claim 521, 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 storing said one or more outcome evaluation modules and said one or more probability update modules;

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

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

534. The processing device of claim 521, 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 outcome values comprises a plurality of outcome values, the processing device further comprising:

a server storing said one or more probability update modules;

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

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

535. The processing device of claim 520, 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 outcome values comprises a plurality of outcome values, 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 storing 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 plurality of user actions, said plurality of computers respectively storing 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.

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

receiving 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 plurality of user actions;

determining one or more outcome values based on said selected one or more processor actions and said plurality of weighted user actions; and

updating said action probability distribution based on said outcome value.

537. The method of claim 536, wherein said plurality of user actions is received from a plurality of users.

538. The method of claim 537, wherein said weighting is based on a skill level of said plurality of users.

539. The method of claim 536, wherein said one or more selected processor actions is selected in response to said plurality of user actions.

540. The method of claim 536, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

541. The method of claim 536, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

542. The method of claim 536, wherein said one or more outcome values comprises a single outcome value corresponding to said plurality of user actions.

543. The method of claim 536, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to said plurality of user actions.

544. The method of claim 536, further comprising modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

545. The method of claim 544, further comprising generating 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.

546. The method of claim 544, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of user actions.

547. The method of claim 544, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of user actions.

548. The method of claim 544, wherein said modification comprises modifying said weighting of said plurality of user actions.

549. The method of claim 544, wherein said modification comprises modifying a subsequently performed action selection.

550. The method of claim 544, wherein said modification comprises modifying a subsequently performed outcome value determination.

551. The method of claim 544, wherein said modification comprises modifying a subsequently performed action probability distribution update.

552. The method of claim 544, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

553. The method of claim 544, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

554. The method of claim 536, wherein said action probability distribution is updated using a learning automaton.

555. The method of claim 536, wherein said processing device is a computer game, said user actions are player actions, and said processor actions are game actions.

556. 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 received user actions, and for determining one or more outcome values based on said selected one or more processor actions and said plurality of weighted user actions; and

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

557. The processing device of claim 556, wherein said plurality of user actions is received from a plurality of users.

558. The processing device of claim 557, wherein said weighting is based on a skill level of said plurality of users.

559. The processing device of claim 556, wherein said action selection module is configured for selecting said one or more selected processor actions in response to said plurality of user actions.

560. The processing device of claim 556, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

561. The processing device of claim 556, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

562. The processing device of claim 556, wherein said one or more outcome values comprises a single outcome value corresponding to said plurality of user actions.

563. The processing device of claim 556, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to said plurality of user actions.

564. The processing device of claim 556, further comprising an intuition module configured for modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

565. The processing device of claim 564, wherein said intuition module is further configured for generating 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 processing device of claim 564, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of user actions.

567. The processing device of claim 564, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of user actions.

568. The processing device of claim 564, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

569. The processing device of claim 564, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

570. The processing device of claim 564, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

571. The processing device of claim 556, wherein said probability update module comprises a learning automaton.

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

receiving 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 plurality of user actions;

comparing said determined success ratio to a reference success ratio;

determining an outcome value based on said success ratio comparison; and

updating said action probability distribution based on said outcome value.

573. The method of claim 572, wherein said plurality of user actions is received from a plurality of users.

574. The method of claim 572, wherein said plurality of user actions is received from a single user.

575. The method of claim 572, wherein said reference success ratio is a simple majority.

576. The method of claim 572, wherein said reference success ratio is a minority.

577. The method of claim 572, wherein said reference success ratio is a super majority.

578. The method of claim 572, wherein said reference success ratio is a unanimity.

579. The method of claim 572, wherein said reference success ratio is an equality.

580. The method of claim 572, wherein said selected processor action is selected in response to said plurality of user actions.

581. The method of claim 572, further comprising modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

582. The method of claim 581, further comprising generating 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.

583. The method of claim 581, wherein said modification comprises modifying said reference success ratio.

584. The method of claim 581, wherein said modification comprises modifying a subsequently performed action selection.

585. The method of claim 581, wherein said modification comprises modifying a subsequently performed outcome value determination.

586. The method of claim 581, wherein said modification comprises modifying a subsequently performed action probability distribution update.

587. The method of claim 581, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

588. The method of claim 581, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

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

590. The method of claim 572, wherein said processing device is a computer game, said user actions are player actions, and said processor actions are game actions.

591. 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 value based on said success ratio comparison; and

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

592. The processing device of claim 591, wherein said plurality of user actions is received from a plurality of users.

593. The processing device of claim 591, wherein said plurality of user actions is received from a single user.

594. The processing device of claim 591, wherein said reference success ratio is a simple majority.

595. The processing device of claim 591, wherein said reference success ratio is a minority.

596. The processing device of claim 591, wherein said reference success ratio is a super majority.

597. The processing device of claim 591, wherein said reference success ratio is a unanimity.

598. The processing device of claim 591, wherein said reference success ratio is an equality.

599. The processing device of claim 591, wherein said action selection module is configured for selecting said processor action in response to said plurality of user actions.

600. The processing device of claim 591, further comprising an intuition module configured for modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

601. The processing device of claim 600, wherein said intuition module is further configured for generating 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.

602. The processing device of claim 600, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of user actions.

603. The processing device of claim 600, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of user actions.

604. The processing device of claim 600, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

605. The processing device of claim 600, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

606. The processing device of claim 600, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

607. The processing device of claim 591, wherein said probability update module comprises a learning automaton.

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

receiving 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 value based on said success determination; and

updating said action probability distribution based on said outcome value.

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

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

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

612. The method of claim 608, 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.

613. The method of claim 608, 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.

614. The method of claim 608, wherein said selected processor action is selected in response to said plurality of user actions.

615. The method of claim 608, further comprising modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

616. The method of claim 615, further comprising generating 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 method of claim 615, wherein said modification comprises modifying said relative success level.

618. The method of claim 615, wherein said modification comprises modifying a subsequently performed action selection.

619. The method of claim 615, wherein said modification comprises modifying a subsequently performed outcome value determination.

620. The method of claim 615, wherein said modification comprises modifying a subsequently performed action probability distribution update.

621. The method of claim 615, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

622. The method of claim 615, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

623. The method of claim 608, wherein said action probability distribution is updated using a learning automaton.

624. The method of claim 608, wherein said processing device is a computer game, said user actions are player actions, and said processor actions are game actions.

625. 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 value based on said success determination; and

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

626. The processing device of claim 625, wherein said reference success level is a greatest success.

627. The processing device of claim 625, wherein said reference success level is a least success.

628. The processing device of claim 625, wherein said reference success level is an average success.

629. The processing device of claim 625, 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.

630. The processing device of claim 625, 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.

631. The processing device of claim 625, wherein said action selection module is configured for selecting said selected processor action in response to said plurality of user actions.

632. The processing device of claim 625, further comprising an intuition module configured for modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

633. The processing device of claim 632, wherein said intuition module is further configured for generating 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.

634. The processing device of claim 632, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of user actions.

635. The processing device of claim 632, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of user actions.

636. The processing device of claim 632, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

637. The processing device of claim 632, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

638. The processing device of claim 632, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

639. The processing device of claim 625, wherein said probability update module comprises a learning automaton.

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

receiving one or more user actions;

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 outcome values based on said one or more linked processor actions and said one or more user actions; and

updating said action probability distribution based on said one or more outcome values.

641. The method of claim 640, wherein said plurality of user parameters comprises a plurality of user actions.

642. The method of claim 640, wherein said plurality of user parameters comprises a plurality of users.

643. The method of claim 640, wherein said plurality of processor actions is linked to another plurality of user parameters.

644. The method of claim 643, wherein said plurality of user parameters comprises a plurality of user actions, and said other plurality of user parameters comprises a plurality of users.

645. The method of claim 640, wherein said selected one or more processor actions is selected in response to said one or more user actions.

646. The method of claim 640, wherein said one or more user actions comprises a plurality of user actions.

647. The method of claim 646, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

648. The method of claim 646, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

649. The method of claim 646, wherein said one or more outcome values comprises a single outcome value corresponding to said plurality of user actions.

650. The method of claim 646, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to said plurality of user actions.

651. The method of claim 640, further comprising modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

652. The method of claim 651, further comprising generating 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.

653. The method of claim 651, wherein said modification comprises modifying said reference success ratio.

654. The method of claim 651, wherein said modification comprises modifying a subsequently performed action selection.

655. The method of claim 651, wherein said modification comprises modifying a subsequently performed outcome value determination.

656. The method of claim 651, wherein said modification comprises modifying a subsequently performed action probability distribution update.

657. The method of claim 651, wherein said modification comprises selecting one of a predetermined plurality of algorithms employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

658. The method of claim 651, wherein said modification comprises modifying a parameter of an algorithm employed by said one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates.

659. The method of claim 640, wherein said action probability distribution is updated using a learning automaton.

660. The method of claim 640, wherein said processing device is a computer game, said one or more user actions are one or more player actions, and said processor actions are game actions.

661. 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 outcome values based on 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 outcome values.

662. The processing device of claim 661, wherein said plurality of user parameters comprises a plurality of user actions.

663. The processing device of claim 661, wherein said plurality of user parameters comprises a plurality of users.

664. The processing device of claim 661, wherein said outcome evaluation module is configured for linking said plurality of processor actions to another plurality of user parameters.

665. The processing device of claim 664, wherein said plurality of user parameters comprises a plurality of user actions, and said other plurality of user parameters comprises a plurality of users.

666. The processing device of claim 661, 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.

667. The processing device of claim 661, wherein said one or more user actions comprises a plurality of user actions.

668. The processing device of claim 667, wherein said selected one or more processor actions comprises a single processor action corresponding to said plurality of user actions.

669. The processing device of claim 667, wherein said selected one or more processor actions comprises a plurality of processor actions respectively corresponding to said plurality of user actions.

670. The processing device of claim 667, wherein said one or more outcome values comprises a single outcome value corresponding to said plurality of user actions.

671. The processing device of claim 667, wherein said one or more outcome values comprises a plurality of outcome values respectively corresponding to said plurality of user actions.

672. The processing device of claim 661, further comprising an intuition module configured for modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

673. The processing device of claim 672, wherein said intuition module is further configured for generating 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.

674. The processing device of claim 672, wherein said one or more performance indexes comprises a single performance index corresponding to said plurality of user actions.

675. The processing device of claim 672, wherein said one or more performance indexes comprises a plurality of performance indexes respectively corresponding to said plurality of user actions.

676. The processing device of claim 672, wherein said intuition module is configured for modifying a functionality of said action selection module based on said one or more objectives.

677. The processing device of claim 672, wherein said intuition module is configured for modifying a functionality of said outcome evaluation module based on said one or more objectives.

678. The processing device of claim 672, wherein said intuition module is configured for modifying a functionality of said probability update module based on said one or more objectives.

679. The processing device of claim 661, wherein said probability update module comprises a learning automaton.

680. 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;

generating 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.

681. The method of claim 680, 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.

682. The method of claim 680, wherein said selected phone number set is communicated to a user of said phone number calling system.

683. The method of claim 682, wherein said selected phone number set is displayed to said user.

684. The method of claim 680, wherein said selected phone number set comprises a plurality of selected phone numbers.

685. The method of claim 680, further comprising selecting a phone number from said selected phone number set to make a phone call.

686. The method of claim 680, wherein said selected phone number set corresponds to the highest probability values in said phone number probability distribution.

687. The method of claim 680, further comprising placing said selected phone number set in an order according to corresponding probability values.

688. The method of claim 680, wherein said identified phone number is associated with an outgoing phone call.

689. The method of claim 680, wherein said identified phone number is associated with an incoming phone call.

690. The method of claim 680, wherein said phone number probability distribution is modified by updating said phone number probability distribution.

691. The method of claim 690, wherein said phone number probability distribution update comprises a reward-inaction update.

692. The method of claim 680, wherein said phone number probability distribution is modified by increasing a probability value.

693. The method of claim 680, wherein said phone number probability distribution is modified by adding a probability value.

694. The method of claim 693, wherein said phone number probability distribution is modified by replacing a probability value with said added probability value.

695. The method of claim 680, wherein said plurality of probability values correspond to all phone numbers within said phone number list.

696. The method of claim 680, wherein said plurality of probability values correspond only to said plurality of phone numbers.

697. The method of claim 680, wherein said performance index is instantaneous.

698. The method of claim 680, wherein said performance index is cumulative.

699. The method of claim 681, wherein said phone number probability distribution is modified by updating it if said identified phone number matches said any listed phone number.

700. The method of claim 699, 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.

701. The method of claim 700, wherein said phone number probability distribution update comprises a reward-inaction update.

702. The method of claim 701, wherein a corresponding probability value is rewarded if said identified phone number matches said any listed phone number.

703. The method of claim 681, wherein said phone number probability distribution is modified by increasing a corresponding probability value if said identified phone number matches said any listed phone number.

704. The method of claim 681, 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.

705. The method of claim 704, 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.

706. The method of claim 680, wherein said phone number calling system comprises a phone.

707. The method of claim 680, wherein said phone number calling system comprises a mobile phone.

708. The method of claim 680, 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.

709. The method of claim 708, 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.

710. The method of claim 708, 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.

711. The method of claim 708, 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.

712. The method of claim 680, wherein said action probability distribution is updated using a learning automaton.

713. The method of claim 680, wherein said action probability distribution is purely frequency based.

714. The method of claim 713, wherein said action probability distribution is based on a moving average.

715. 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.

716. The phone number calling system of claim 715, wherein said probability learning module is further configured for generating 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.

717. The phone number calling system of claim 715, further comprising a display for displaying said favorite phone numbers.

718. The phone number calling system of claim 715, further comprising one or more selection buttons configured for selecting one of said favorite phone numbers to make a phone call.

719. The phone number calling system of claim 715, wherein said identified phone numbers are associated with outgoing phone calls.

720. The phone number calling system of claim 715, wherein said identified phone numbers are associated with incoming phone calls.

721. The phone number calling system of claim 715, wherein said probabilistic learning module comprises:

an action 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, wherein a phone list contains at least said plurality of phone numbers;

an outcome evaluation module configured for determining if identified phone numbers associated with said phone calls match 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.

722. The phone number calling system of claim 721, wherein said favorite phone numbers correspond to the highest probability values in said phone number probability distribution.

723. The phone number calling system of claim 721, wherein said phone number selection module is further configured for placing said favorite numbers in an order according to corresponding probability values.

724. The phone number calling system of claim 721, wherein said intuition module is configured for modifying said probability update module by directing it to update said phone number probability distribution if any of said identified phone numbers matches said any listed phone number.

725. The phone number calling system of claim 724, wherein said probability update module is configured for updating said phone number probability using a reward-inaction algorithm.

726. The phone number calling system of claim 725, wherein said probability update module is configured for rewarding a corresponding probability value.

727. The phone number calling system of claim 721, 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 plurality of phone numbers matches a listed phone number corresponding to one of said favorite phone numbers.

728. The phone number calling system of claim 721, wherein said intuition module is configured for modifying said probability update module by increasing a corresponding probability value if any of said identified phone numbers matches said any listed phone number.

729. The phone number calling system of claim 721, 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.

730. The phone number calling system of claim 729, 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.

731. The phone number calling system of claim 721, wherein said plurality of probability values correspond to all phone numbers within said phone number list.

732. The phone number calling system of claim 721, wherein said plurality of probability values correspond only to said plurality of listed phone numbers.

733. The phone number calling system of claim 716, wherein said performance index is instantaneous.

734. The phone number calling system of claim 716, wherein said performance index is cumulative.

735. The phone number calling system of claim 715, 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.

736. The phone number calling system of claim 735, wherein said first time period includes weekdays, and said second time period includes weekends.

737. The phone number calling system of claim 735, wherein said first time period includes days, and said second time period includes evenings.

738. The phone number calling system of claim 715, wherein said probabilistic learning module comprises a learning automaton.

739. The phone number calling system of claim 715, wherein said probabilistic learning module is purely frequency-based.

740. 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.

741. The phone number calling system of claim 740, wherein said learning module and said intuition module are self-contained in a single device.

742. The phone number calling system of claim 740, wherein said learning module and said intuition module are contained in a telephone.

743. The phone number calling system of claim 742, wherein said telephone is a mobile telephone.

744. The phone number calling system of claim 740, wherein said learning module and said intuition module are contained in a server.

745. The phone number calling system of claim 740, wherein said learning module and said intuition module are distributed within a server and a phone.

746. The phone number calling system of claim 740, wherein said probabilistic learning module comprises a learning automaton.

747. The phone number calling system of claim 740, wherein said probabilistic learning module is purely frequency-based.

748. A method of providing learning capability to a phone number calling system, comprising:

receiving a plurality of phone numbers;

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.

749. The method of claim 748, further comprising updating a phone number probability distribution containing said plurality of priority values using a learning automaton.

750. The method of claim 748, 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.

751. The method of claim 750, wherein each of said plurality of priority values is based on a total number of times said associated phone number is received during a specified time period.

752. The method of claim 748, wherein said selected phone number set is displayed to said user.

753. The method of claim 748, wherein said selected phone number set comprises a plurality of selected phone numbers.

754. The method of claim 748, further comprising selecting a phone number from said selected phone number set to make a phone call.

755. The method of claim 748, wherein said selected phone number set corresponds to the highest priority values.

756. The method of claim 748, further comprising placing said selected phone number set in an order according to corresponding priority values.

757. The method of claim 748, wherein said plurality of phone numbers is associated with outgoing phone calls.

758. The method of claim 748, wherein said plurality of phone numbers is associated with incoming phone calls.

759. The method of claim 748, wherein said phone number calling system comprises a phone.

760. The method of claim 748, wherein said phone number calling system comprises a mobile phone.