Towards a novel Integrated Neuro-Cognitive Architecture (INCA)

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 1902-1909

  Oentaryo, Richard J ^a   Pasquier, Michel ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTELLIGENT AGENTS; INTELLIGENT BUILDINGS; NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE RESEARCHES; COGNITIVE ABILITIES; COGNITIVE ARCHITECTURES; COGNITIVE FUNCTIONS; CONNECTIONIST LEARNINGS; FUNCTIONAL ASPECTS; LEARNING MEMORIES; MODELING APPROACHES; SELF-ORGANIZING; SYMBOLIC KNOWLEDGES;

COGNITIVE SYSTEMS;

EID: 56349104775     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4634058     Document Type: Conference Paper

References (39)

1. A. Newell, Unified Theories of Cognition: Harvard University Press, 1990.
2. M. Minsky, "Scientist on the Set: An Interview with Marvin Minsky," in HAL's Legacy: 2001's Computer as Dream and Reality, D. G. Stork, Ed.: MIT Press, 1997, pp. 15-30.
3. E. R. Kandel, J. H. Schwartz, and T. M. Jessel, Principles of Neural Science, 4th ed. New York: McGraw-Hill, 2000.
4. J. R. Anderson, Learning and Memory, 2nd ed. New York: Wiley, 2000.
5. W. Duch, R. J. Oentaryo, and M. Pasquier, "Cognitive architectures: Where do we go from there?," in Artificial General Intelligence 2008, P. Wang, B. Goertzel, and S. Franklin, Eds. Amsterdam: IOS Press, 2008, pp. 122-136.
6. J. E. Laird, P. S. Rosenbloom, and A. Newell, "Soar: An architecture for general intelligence," Artificial Intelligence, vol. 33, pp. 1-64, 1987.
7. P. Langley, "An adaptive architecture for physical, agents," in Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Compiegne, France, 2005, pp. 18-25.
8. P. Langley and D. Choi, "Learning recursive control programs from problem solving," Journal of Machine Learning Research, vol. 7, pp. 493-518, 2006.
9. R. Hecht-Nielsen, "A cortronic neural network: models of cortical function," in Proceedings of the International Conference on Neural Information Processing, S. Usui and T. Omori, Eds. KitaKyushu, Japan: IOA Press, 1998.
10. R. Hecht-Nielsen, Confabulation Theory: The Mechanism of Thought: Springer, 2007.
11. R. C. O'Reilly, T. S. Braver, and J. D. Cohen, "A biologically-based computational model of working memory.," in Models of Working Memory, A. Miyake and P. Shah, Eds.: Cambridge University Press, 1999, pp. 375-411.
12. R. C. O'Reilly and Y. Munakata, Computational Explorations in Cognitive Neuroscience: Understanding of the Mind by Simulating the Brain. Cambridge, MA: MIT Press, 2000.
13. R. Sun and F. Alexandre, Connectionist Symbolic Integration. Hillsdale, NJ: Erlbaum, 1997.
14. J. R. Anderson and C. Lebiere, "The Newell test for a theory of cognition," Behavioral and Brain Science, vol. 26, pp. 587-637, 2003.
15. J. R. Anderson, D. Bothell, M. D. Byrne, S. Douglass, C. Lebiere, and Y. Qin, "An integrated theory of the mind," Psychological Review, vol. 111, pp. 1036-1060, 2004.
16. S. Franklin, "The LIDA architecture: Adding new modes of learning to an intelligent, autonomous, software agent," in Proceedings of the International Conference on Integrated Design and Process Technology. San Diego: Society for Design & Process Science, 2006.
17. S. Franklin, A. Kelemen, and L. McCauley, "IDA: A cognitive agent architecture," in Proceedings of the IEEE Conference on Systems, Man and Cybernetics, vol. 3. San Diego, CA, 1998, pp. 2646-2651.
18. R. Sun and T. Peterson, "Autonomous learning of sequential tasks: Experiments and analyses," IEEE Transactions on Neural Networks, vol. 9, pp. 1217-1234, 1998.
19. H. Eichenbaum, The Cognitive Neuroscience of Memory: An Introduction. New York: Oxford University Press, 2002.
20. R. J. Oentaryo, M. Pasquier, and C. Quek, "GenSoFNN-Yager: A novel brain-inspired generic self-organizing neuro-fuzzy system realizing Yager inference," Expert Systems with Applications, to be published.
21. J. Sim, W. L. Tung, and C. Quek, "FCMAC-Yager: A novel Yager-inference-scheme-based fuzzy CMAC," IEEE Transactions on Neural Networks, vol. 17, pp. 1394-1410, 2006.
22. R. J. Oentaryo, "Metacognitive intelligent system in complex environments," Nanyang Technological University, Singapore, First Year Ph.D. Confirmation Report, 2006.
23. J. LeDoux, "Brain mechanisms of emotion and emotional learning," Current Opinion in Neurobiology, vol. 2, pp. 191-197, 1992.
24. E. Lumanpauw, M. Pasquier, and R. J. Oentaryo, "Generic GA-based meta-level parameter optimization for pattern recognition systems," in Proceedings of the IEEE Congress on Evolutionary Computation. Singapore, 2007, pp. 1593-1600.
25. W. Duch, "Filter methods," in Feature Extraction, Foundations and Applications, Series Studies in Fuzziness and Soft Computing, I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Eds.: Physica-Verlag, Springer, 2006, pp. 89-118.
26. W. Duch, R. Adamczak, and N. Jankowski, "Initialization of adaptive parameters in density networks," in Proceedings of the Conference on Neural Networks and Their Applications. Kule, 1997, pp. 99-104.
27. J. L. McClelland, B. L. McNaughton, and R. C. O'Reilly, "Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory," Psychological Review, vol. 102, pp. 419-457, 1995.
28. M. Pasquier, C. Quek, and M. Toh, "Fuzzylot: A novel self-organising fuzzy-neural rule-based pilot system for automated vehicles," Neural Networks, vol. 14, pp. 1099-1112, 2001.
29. M. Pasquier and R. J. Oentaryo, "Learning to drive the human way: A step towards intelligent vehicles," International Journal of Vehicle Autonomous Systems, Special Issue on Advances in Autonomous Vehicle Technologies for Urban Environment, vol. 6, pp. 24-47, 2008.
30. T. K. Kohonen, "Self-organized formation of topologically correct feature maps," Biological Cybernetics, vol. 43, pp. 59-69, 1982.
31. Z. Pawlak, "Rough sets," International Journal of Computer and Information Sciences, vol. 11, pp. 341-356, 1982.
32. R. Sun, "Supplementing neural reinforcement learning with symbolic methods," in Hybrid Neural Systems, S. Wermter and R. Sun, Eds. Berlin: Springer-Verlag, 2000, pp. 333-347.
33. M. Chan, M. Pasquier, and D. Partouche, "An intelligent driving system for automatically anticipating and negotiating road curves," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, USA, 2007, pp. 117-122.
34. B. J. Baars and S. Franklin, "How conscious experience and working memory interact," Trends in Cognitive Sciences, vol. 7, pp. 166-172, 2003.
35. R. Sun and L. Bookman, Architectures Incorporating Neural and Symbolic Processes. Boston:. Kluwer, 1994.
36. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice Hall, 1996.
37. A. Sloman, "What sort of architecture is required for a humanlike agent," in Foundations of Rational Agency, M. Wooldridge and A. Rao-Dordrecht, Eds.: Kluwer Academic Publishers, 1999.
38. R. C. Atkinson and R. M. Shiffrin, "Human memory: A proposed system and its control processes," in The Psychology of Learning and Memory: Advances in Research and Theory, K. W. Spence and J. T. Spence, Eds. New York: Academic Press, 1968, pp. 89-195.
39. B. J. Baars, A Cognitive Theory of Consciousness. Cambridge, MA: Cambridge University Press, 1988.