Reluctant episodic memory (REM) to store experiences of...

Data processing: artificial intelligence – Knowledge processing system – Creation or modification

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S045000

Reexamination Certificate

active

07974938

ABSTRACT:
A method and system for storing episodic sequences (events and actions). The system learns episodic sequencing by observing real-world events and actions or by receiving fact data from a database storing common sense facts. The episodic sequences are classified into events and actions, processed to indicate correlations and causality between the events and actions, and generated into linked graphs. The linked graphs may then be used to draw inferences, recognize patterns, and make decisions.

REFERENCES:
patent: 5745895 (1998-04-01), Bingham et al.
patent: 2005/0271279 (2005-12-01), Fujimura et al.
Habetha et al, Fuzzy Rule-Based Mobility and Load Management for Self-Organizing Wireless Networks, International Journal of Wireless Information Networks, vol. 9, No. 2, Apr. 2002, pp. 119-140.
Bentivegna, D.C. et al., “Humanoid Robot Learning and Game Playing Using PC-Based Vision” , Intelligent Robots and System, Sep./Oct. 2002, pp. 2449-2454, vol. 3, IEEE/RSJ International Conference, Lausanne, Switzerland.
Bentivegna, D.C. et al., “Learning From Observation Using Primitives,” Thesis, Jul. 2004, pp. 1-146, College of Computing, Georgia Institute of Technology.
Atkinson, R.C. et al., “Human memory: A proposed system and its control process,” The Psychology of Learning and Motivation, 1968, pp. 117-123, 164-910, vol. 2, Academic Press.
Gupta, R. et al., “Knowledge representation and bayesian inference for response to situations,” in Workshop on Link Analysis (AAAI-05), Jul. 10, 2005.
McCarthy, J. et al, “Some philosophical problems from the standpoint of artificial intelligence,” [online] [Retrieved on Jan. 30, 2008], Retrieved from the Internet: <URL: http://www-formal.stanford.edu/jmc/mcchay69.html>.
Pazzani, M., “A Computational Theory of Learning Causal Relationship”, Cognitive Science 15, 1991, pp. 401-424, [online] [Retrieved on Jan. 30, 2008], Retrieved from the Internet <URL: http://www.ics.uci.edu/˜pazzani/Publications/cogscij.pdf>.
Schank, R.C., “Dynamic Memory: A theory of reminding and learning in computers and people,” 1982, pp. 197-226, Cambridge University Press.
Steyvers, M. et al., “Inferring Causal Networks from Observations and Interventions”, Cognitive Science, 2003, pp. 453-489, vol. 27, [online] [Retrieved on Jan. 30, 2008], Retrieved from the Internet <URL: http://web.mit.edu/cocosci/Papers/steyvers-etal-2003.pdf>.
Tenenbaum, J. et al., “Intuitive Theories as Grammars for Causal Inference” [online] [Retrieved on Jan. 30, 2008], Retrieved from the Internet <URL:http://web.mit.edu/cocosci/Papers/tgn-grammar.pdf>.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Reluctant episodic memory (REM) to store experiences of... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Reluctant episodic memory (REM) to store experiences of..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reluctant episodic memory (REM) to store experiences of... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-2728459

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.