A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures

Neurocomputing

Volumn 74, Issue 1-3, 2010, Pages 30-49

  Goertzel, Ben ^a,b   Lian, Ruiting ^b   Arel, Itamar ^c   de Garis, Hugo ^b   Chen, Shuo ^b  

^a NOVAMENTE LLC   (United States)

^b XIAMEN UNIVERSITY   (China)

^c University of Tennessee   (United States)

Author keywords

Artificial brains; Cognitive architectures

Indexed keywords

ARCHITECTURAL LEVELS; ARTIFICIAL BRAIN; BIOLOGICALLY INSPIRED; BRAIN FUNCTIONS; BRAIN REGIONS; COGNITIVE ARCHITECTURES; DEVELOPMENTAL ROBOTICS; HUMAN BRAIN; HYBRID ARCHITECTURES; INTERNAL DYNAMICS; INTERNAL STRUCTURE; NEURAL PROCESS; SELECTED EXAMPLES; STATE OF THE ART;

BRAIN;

ARCHITECTURE;

ARTICLE; ARTIFICIAL BRAIN; ARTIFICIAL NEURAL NETWORK; ASSOCIATIVE MEMORY; ATTENTION; BEHAVIOR; BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURE; BRAIN FUNCTION; COGNITION; CREATIVITY; DECLARATIVE KNOWLEDGE; DECLARATIVE MEMORY; EMOTION; ENVIRONMENT; EPISODIC MEMORY; HUMAN; INFORMATION PROCESSING; INTELLIGENCE; KNOWLEDGE; LANGUAGE; LEARNING; MACHINE LEARNING; MOTIVATION; MOTOR ACTIVITY; PATTERN RECOGNITION; PERCEPTION; PRIORITY JOURNAL; PROBLEM SOLVING; PROCEDURAL KNOWLEDGE; PROCEDURAL MEMORY; REINFORCEMENT LEARNING; ROBOTICS; SEMANTIC MEMORY; SENSORIMOTOR FUNCTION; SIMULATION; SYMBOLISM; VIRTUAL REALITY; VISUAL MEMORY; WORKING MEMORY;

EID: 78649498001     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.08.012     Document Type: Article

References (65)

1. W. Duch, R. Oentaryo, M. Pasquier, Cognitive architectures: where do we go from here? Proceedings of the Second Conference on AGI, 2008.
2. Pelikan M. Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms 2005, Springer.
3. Laird J., Rosenbloom P., Newell A. Soar: an architecture for general intelligence. Artificial Intelligence 1987, 33.
4. Anderson J.R., Lebiere C. The newell test for a theory of cognition. Behavioral and Brain Sciences 2003, 26.
5. Gray W., Schoelles M., Myers C. Meeting newells other challenge: cognitive architectures as the basis for cognitive engineering. Behavioral and Brain Sciences 2009.
6. Meyer D.E., Kieras D.E. A computational theory of executive cognitive processes and multiple-task performance: Part 1. Psychological Review 1997, 104.
7. P. Langley, An adaptive architecture for physical agents, in: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2005.
8. Shapiro S., et al. Metacognition in sneps. AI Magazine 2007, 28.
9. Anderson J.R., Fincham J.M., Qin Y., Stocco A. A central circuit of the mind. Trends in Cognitive Science 2008, 12(4):136-143.
10. Hawkins J., Blakeslee S. On intelligence. Brown Walker 2006.
11. I. Arel, D. Rose, R. Coop, Destin: a scalable deep learning architecture with application to high-dimensional robust pattern recognition, in: Proceedings of the AAAI Workshop on Biologically Inspired Cognitive Architectures, November 2009.
12. I. Arel, D. Rose, T. Karnowski, A deep learning architecture comprising homogeneous cortical circuits for scalable spatiotemporal pattern inference, in: NIPS 2009 Workshop on Deep Learning for Speech Recognition and Related Applications, December 2009.
13. O'Reilly R.C., Braver T.S., Cohen J.D. A biologically-based computational model of working memory, in: Models of Working Memory 1999, 375-411. A. Miyake, P. Shah (Eds.).
14. J. Fleischer, J. Gally, G. Edelman, and J. Krichmar, Retrospective and prospective responses arising in a modeled hippocampus during maze navigation by a brain-based device, in: Proceedings of the National Academy of Sciences, 104, 2007.
15. Modayil J., Kuipers B. Autonomous development of a grounded object ontology by a learning robot. AAAI-07 2007.
16. J. Mugan, B. Kuipers, Towards the application of reinforcement learning to undirected develop-mental learning, in: International Conference on Epigenetic Robotics, 2008.
17. Mugan J., Kuipers B. Autonomously learning an action hierarchy using a learned qualitative state representation. IJCAI-09 2009.
18. Hinton G.E., Osindero S., Teh Y. A fast learning algorithm for deep belief nets. Neural Computation 2006, 18:1527-1554.
19. LeCun Y., Boser B., Denker J.S., et al. Handwritten digit recognition with a back-propagation network. Advances in Neural Information Processing Systems 1990, 2.
20. Juergen Schmidhuber, Curious model-building control systems, in: Proceedings of the International Joint Conference on Neural Networks, 1991.
21. Juergen Schmidhuber, A possibility for implementing curiosity and boredom in model-building neural controllers, in: Proceedings of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, 1991.
22. Juergen Schmidhuber, Reinforcement-driven information acquisition in non-deterministic environments, in: Proceedings of the ICANN'95, 1995.
23. Schmidhuber Juergen Exploring the Predictable, Advances in Evolutionary Computing 2002, Springer.
24. J. Han, S. Zeng, K. Tham, M. Badgero, J.Weng, Dav: a humanoid robot platform for autonomous mental development, in: Proceedings of the Second International Conference on Development and Learning, 2002.
25. J. Weng, W.S. Hwang, Y. Zhang, C. Yang, R. Smith, Developmental humanoids: humanoids that develop skills automatically, in: Proceedings of the First IEEE-RAS International Conference on Humanoid Robots, 2000.
26. Weng J., Hwangi W.S. From neural networks to the brain: autonomous mental development. IEEE Computational Intelligence Magazine 2006.
27. A. Baranes, P.-Y. Oudeyer, R-iac: robust intrinsically motivated active learning, in: Proceedings of the IEEE International Conference on Learning and Development, vol. 33, Shangha, China, 2009.
28. Kaplan F. Neurorobotics: an experimental science of embodiment. Frontiers in Neuroscience 2008.
29. Oudeyer P., Kaplan F. Discovering communication. Connection Science 2006.
30. G. Metta, G. Sandini, D. Vernon, L. Natale, F. Nori, The icub humanoid robot: an open platform for research in embodied cognition, in: Performance Metrics for Intelligent Systems Workshop (PerMIS 2008), 2008.
31. Schmidhuber J. Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science 2006.
32. B. Bakker, J. Schmidhuber, Hierarchical reinforcement learning based on subgoal discovery and subpolicy specialization, in: Proceedings of the Eighth Conference on Intelligent Autonomous Systems, 2004.
33. B. Bakker, V. Zhumatiy, G. Gruener, J. Schmidhuber, Quasi-online reinforcement learning for robots, in: Proceedings of the International Conference on Robotics and Automation, 2006.
34. J. Simsek, A. Barto, An intrinsic reward mechanism for efficient exploration, in: Proceedings of the Twenty-Third International Conference on Machine Learning, 2006.
35. S. Singh, A. Barto, N. Chentanez, Intrinsically motivated reinforcement learning, in: Proceedings of the Neural Information Processing Systems, vol. 17, 2005.
36. Lee M.H., Meng Q., Chao F. Developmental learning for autonomous robots. Robotics and Autonomous Systems 2007, 55:750-759.
37. Lee M.H., Meng Q., Chao F. Staged competence learning in developmental robotics. Adaptive Behavior 2007, 15(3):241-255.
38. Nilsson N. The physical symbol system hypothesis: status and prospects, 50 Years of AI, Festschrift. Lecture Notes in Artificial Intelligence 2009, 33:48-50.
39. Sun R., Zhang X. Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research 2004, 5.
40. Nestor A., Kokinov B. Towards active vision in the dual cognitive architecture. International Journal on Information Theories and Applications 2004, 11.
41. S. Franklin, The lida architecture: adding new modes of learning to an intelligent, autonomous, software agent, in: International Conference on Integrated Design and Process Technology, 2006.
42. Bach J. Principles of Synthetic Intelligence 2009, Oxford University Press.
43. N. Cassimatis. Adaptive algorithmic hybrids for human-level artificial intelligence, in: B. Goertzel, P. Wang (Eds.), Advances in Artificial General Intelligence, IOS Press, 2007, pp. 151-163.
44. Shastri L., Ajjanagadde V. From simple associations to systematic reasoning: a connectionist en-coding of rules, variables, and dynamic bindings using temporal synchrony. Behavioral and Brain Sciences 1993, 16(3).
45. Hofstadter D. Fluid Concepts and Creative Analogies 1996, Basic Books.
46. B. Goertzel, The hidden pattern. Brown Walker, 2006.
47. B. Goertzel, Opencog prime: a cognitive synergy based architecture for embodied artificial general intelligence, in: Proceedings of the ICCI-09, Hong Kong, 2009.
48. Marvin M. The Society of Mind March 15, 1988, Simon and Schuster, New York.
49. Marvin M. The Emotion Machine 2006, Simon and Schuster.
50. P. Singh, EM-ONE: an architecture for reflective commonsense thinking, Ph.D. Thesis, MIT, June 2005.
51. Albus J., Meystel A. Engineering of Mind: An Introduction to the Science of Intelligent Systems 2001, Wiley and Sons.
52. Friedlander D., Franklin S. LIDA and a theory of mind, in: Artificial General Intelligence (AGI-08) 2008, IOS Press, Memphis, TN, USA. B. Goertzel, P. Wang (Eds.).
53. S. Franklin, The lida architecture: adding new modes of learning to an intelligent, autonomous, software agent, in: International Conference on Integrated Design and Process Technology, 2006.
54. Tulving E., Craik R. The Oxford Handbook of Memory 2005, Oxford University Press.
55. Goertzel B., Ikle I.G.M., Heljakka A. Probabilistic Logic Networks 2008, Springer.
56. Fauconnier G., Turner M. The way we think: conceptual blending and the mind's hidden complexities. Basic 2002.
57. M. Looks, Competent program evolution, Ph.D. Thesis, Computer Science Department, Washington University, 2006.
58. B. Goertzel C.P., et al, An integrative methodology for teaching embodied non-linguistic agents, applied to virtual animals in second life, in: Proceedings of the AGI-08, 2008.
59. B. Goertzel, J. Pitt, M. Ikle, C. Pennachin, R. Liu, Glocal memory: a design principle for artificial brains and minds, Neurocomputing, this issue, doi:. http://1016/j.neucom.2009.10.033.
60. Bach J. Principles of Synthetic Intelligence 2009, Oxford University Press.
61. 〈〉. http://ccrg.cs.memphis.edu/tutorial/correlates.html.
62. B. Goertzel, H. Pinto, C. Pennachin, Using dependency parsing and probabilistic inference to extract relationships between genes, proteins and malignancies implicit among multiple biomedical research abstracts, in: Proceedings of the BioNLP Workshop/HLT-NAACL, 2006.
63. B. Goertzel H. de Garis, in: XIA-MAN: an extensible, integrative architecture for intelligent humanoid robotics, in: Proceedings of the BICA-08, 2008, pp. 86-90.
64. Albus J.S., Barbera A.J. RCS: a cognitive architecture for intelligent multi-agent systems. Annual Reviews in Control 2005, 29(1):87-99.
65. J.S. Albus, Reverse engineering the brain, in: Proceedings of the AAAI Fall Symposium, Washington, DC, 2008.