Processing device having selectible difficulty levels with intuitive learning capability

• US 8,219,509 B2
• Filed: 12/05/2008
• Issued: 07/10/2012
• Est. Priority Date: 06/26/2001
• Status: Expired due to Term

• Current Assignee: Intuition Intelligence Incorporated
• Original Assignee: Intuition Intelligence Incorporated
• Inventors: Ansari, Arif M, Shiek Ansari, Yusuf Sulaiman M
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Wong, Lut
• Application Number:
  US12/329,374
• Publication Number:
  US 20090209323A1
• Time in Patent Office: 1,313 Days
• Field of Search: 706/12
• 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 13/23 for interfacing with the game device, e.g. specific interfaces between game controller and console
• CPC Class Codes: A63F 13/30 Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
• CPC Class Codes: A63F 13/338 using television networks
• CPC Class Codes: A63F 13/45 Controlling the progress of the video game
• CPC Class Codes: A63F 13/67 adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
• CPC Class Codes: A63F 13/798 for assessing skills or for ranking players, e.g. for generating a hall of fame computing the game score A63F13/46
• CPC Class Codes: A63F 2300/558 by assessing the players' skills or ranking
• CPC Class Codes: A63F 2300/6027 using adaptive systems learning from user actions, e.g. for skill level adjustment
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/443 OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4751 for defining user accounts, e.g. accounts for children

Abstract

A processing device and a method of providing learning capability thereto are provided. One of a plurality of processor actions is selected based on an action probability distribution comprising a plurality of probability values corresponding to the plurality of processor actions. The plurality of processor actions are associated with a plurality of different difficulty levels. An action performed by a user is identified, an outcome of selected processor action relative to the identified user action is determined, and the action probability distribution is updated based on the outcome and the difficulty level of the selected processor action.

Claims

1. A method of providing learning capability to an educational device, comprising:

selecting one of a plurality of educational tasks to be performed by said user based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of educational tasks, said plurality of educational tasks being associated with a plurality of different difficulty levels said plurality of educational tasks comprising identifying combinations of items;

identifying an educational task performed by a user;

determining an outcome of said selected educational task relative to said identified educational task; and

updating said action probability distribution based on said outcome and said difficulty level of said selected educational task.

2. The method of claim 1, wherein said action probability distribution is shifted from one or more probability values corresponding to one or more educational tasks associated with lesser difficulty levels to one or more probability values corresponding to one or more educational tasks associated with greater difficulty levels if said outcome indicates that said identified educational task is successful relative to said selected educational task.

3. The method of claim 2, wherein said one or more actions educational tasks associated with greater difficulty levels includes an educational task associated with a difficulty level greater than said difficulty level of said selected educational task.

4. The method of claim 2, wherein said one or more educational tasks associated with lesser difficulty levels includes an educational task associated with a difficulty level less than said difficulty level of said selected educational task.

5. The method of claim 1, wherein said action probability distribution is shifted from one or more probability values corresponding to one or more educational tasks associated with greater difficulty levels to one or more probability values corresponding to one or more educational tasks associated with lesser difficulty levels if said outcome indicates that said identified educational task is unsuccessful relative to said selected educational task.

6. The method of claim 5, wherein said one or more educational tasks associated with lesser difficulty levels includes an educational task associated with a difficulty level less than said difficulty level of said educational task.

7. The method of claim 5, wherein said one or more educational tasks associated with greater difficulty levels includes an educational task associated with a difficulty level greater than said difficulty level of said selected educational task.

8. The method of claim 1, wherein said selected educational task corresponds to a pseudo-random selection of a probability value within said action probability distribution.

9. The method of claim 1, wherein said action probability distribution is updated using a learning automaton.

10. A method of providing learning capability to an educational device for increasing the educational level of said user, comprising:

selecting one of a plurality of educational tasks to be performed by said user based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of educational tasks, said plurality of educational tasks being associated with a plurality of different difficulty levels, said plurality of educational tasks comprising identifying combinations of items;

identifying an educational task performed by a user;

determining an outcome of said selected educational task relative to said identified educational task;

updating said action probability distribution based on said outcome; and

modifying one or more of said action selection, outcome determination, and action probability distribution update to increase the educational level of said user.

11. The method of claim 10, wherein said modification comprises selecting one of a predetermined plurality of learning methodologies employed by said action probability distribution update.

12. The method of claim 11, wherein a learning methodology that rewards an educational task having a difficulty level equal to or greater than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is successful relative to said selected educational task.

13. The method of claim 11, wherein a learning methodology that penalizes an educational task having a difficulty level equal to or less than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is successful relative to said selected educational task.

14. The method of claim 11, wherein a learning methodology that rewards an educational task having a difficulty level equal to or less than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is unsuccessful relative to said selected educational task.

15. The method of claim 11, wherein a learning methodology that penalizes an educational task having a difficulty level equal to or greater than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is unsuccessful relative to said selected educational task.

16. The method of claim 10, wherein said action probability distribution is updated using a learning automaton.

17. An educational device for increasing the educational level of said user, comprising:

an action selection module that, when executed by a processor, is configured for selecting one of a plurality of educational tasks to be performed by said user based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of educational tasks, said plurality of educational tasks being associated with a plurality of different difficulty levels, said plurality of educational tasks comprising identifying combinations of items;

an outcome evaluation module that, when executed by said processor, is configured for determining an outcome of said selected educational task relative to an identified educational task performed by the user; and

a probability update module that, when executed by a processor, is configured for updating said action probability distribution based on said outcome and said difficulty level of said selected educational task.

18. The educational device of claim 17, wherein said intuition module is configured for selecting one of a predetermined plurality of learning methodologies employed by said probability update module.

19. The educational device of claim 18, wherein said intuition module is configured for selecting a learning methodology that rewards an educational task having a difficulty level equal to or greater than said difficulty level of said selected educational task if said outcome indicates that said identified educational task is successful relative to said selected educational task.

20. The educational device of claim 18, wherein said intuition module is configured for selecting a learning methodology that penalizes an educational task having a difficulty level equal to or less than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is successful relative to said selected educational task.

21. The educational device of claim 18, wherein said intuition module is configured for selecting a learning methodology that rewards an educational task having a difficulty level equal to or less than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is unsuccessful relative to said selected educational task.

22. The educational device of claim 18, wherein said intuition module is configured for selecting a learning methodology that penalizes an educational task having a difficulty level equal to or greater than said difficulty level of said selected educational task is selected if said outcome indicates that said identified educational task is unsuccessful relative to said selected educational task.

23. The educational device of claim 17, wherein said probability update module is configured for using a learning automaton to update said action probability distribution.