A real-world rational agent: Unifying old and new AI

Cognitive Science

Volumn 27, Issue 4, 2003, Pages 561-590

  Verschure, Paul F M J ^a   Althaus, Philipp ^b  

^a Institute of Neuroinformatics Zurich   (Switzerland)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Artificial intelligence; Cognitive architecture; Computer simulation; Decision making; Intelligent agents; Learning; Machine learning; Neural networks; Neuroscience; Problem solving; Psychology; Robotics; Situated cognition

Indexed keywords

EID: 0037972164     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0364-0213(03)00034-X     Document Type: Article

References (81)

1. Almássy, N. (1993). BugWorld: A distributed environment for the development of control architectures in multi-agent worlds (Technical report 93.32). Department of Computer Science, University Zurich.
2. Anastasia T.J. Patton P.E. Belkacem-Boussaid K. Using Bayes' rule to model multisensory enhancement in the superior colliculus Neural Computation 12 (5) 2000 1165-1187
3. Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
4. Arkin, R. C. (1998). Behavior-based robotics. Cambridge, MA: MIT Press.
5. Bayes T. An essay towards solving a problem in the doctrine of chances Transactions of the Royal Society 53 1763 370-418
6. Brooks R. New approaches to robotics Science 253 1991a 1227-1232
7. Brooks R.A. Intelligence without representation Artificial Intelligence 47 1991b 139-159
8. Burgess A. Visual signal detection. III. On Bayesian use of prior knowledge and cross correlation Journal of Optical Society of America A 2 1985 1498-1507
9. Chandrasekaran, B. (1994). Understanding control at the knowledge level. In Working notes AAAI fall symposium on control of the physical world by intelligent agents (pp. 19-26). AAAI.
10. Chater N. Oaksford M. Ten years of the rational analysis of cognition Trends in Cognitive Science 3 (2) 1999 57-65
11. Clancey, W. J. (1989a). The frame of reference problem in cognitive modeling. In Proceedings of the Annual Conference of the Cognitive Science Society (pp. 107-114). Hillsdale, NJ: Erlbaum.
12. Clancey W.J. The knowledge level reinterpreted: Modeling how systems interact Machine Learning 4 1989b 285-291
13. Clancey, W. J. (1996). Situated cognition: On human knowledge and computer representations. Cambridge: Cambridge University Press.
14. Dennett, D. C. (1988). The intentional stance. Cambridge, MA: Bradford Books/MIT.
15. El-Gamal M.A. Grether D.M. Are people Bayesian? Uncovering behavioral strategies Journal of the American Statistical Association 90 1995 1137-1145
16. Fodor J.A.Pylyshyn Z.W.C Connectionism and cognitive architecture, a critical analysis Cognition 28 1988 3-71
17. Fong C. McCabe K. Are decisions under risk malleable? Proceedings of the National Academy of Sciences of the United States of America 96 (19) 1999 10927-11132
18. Gallistel, C. R. (1990). The organization of learning. Cambridge, MA: MIT Press.
19. Goldstein, L., & Smith, K. (1991). BugWorld a distributed environment for the study of multi-agent learning algorithms (Technical report). Department of Computer Science, UCSC.
20. Haddawy P. An overview of some recent developments in Bayesian problem-solving techniques AI Magazine 20 1999 11-19
21. Harnad S. The symbol grounding problem Physica D 42 1990 335-346
22. Hendriks-Jansen, H. (1996). Catching ourselves in the act. Cambridge MA: MIT press.
23. Herrnstein R.J. On the law of effect Journal of the Experimental Analysis of Behaviour 13 1970 243-266
24. Hofstoetter C. Mintz M. Verschure P.F.M.J. The cerebellum in action: A simulation and robotics study European Journal of Neuroscience 16 2002 1361-1376
25. Hollerman J.R. Schultz W. Dopamine neurons report an error in the temporal prediction of reward during learning Nature Neuroscience 1 1998 304-309
26. Hu H. Brady M. A Bayesian approach to real-time obstacle avoidance for a mobile robot Autonomous Robots 1 1994 69-92
27. Kaelbling L.P. Littman M.L. Moore A.W. Reinforcement learning: A survey Journal of Artificial Intelligence Research 4 1996 237-385
28. Kalman R.E. A new approach to linear filtering and prediction problems ASME Journal of Basic Engineering 82 1960 35-45
29. Knill, D. K., & Richards, W. (1996). Perception on Bayesian inference. Cambridge, MA: Cambridge University Press.
30. Koechlin E. Anton J.L. Burnod Y. Bayesian inference in populations of cortical neurons: A model of motion integration and segmentation in area MT Biological Cybernetics 80 1999 25-44
31. Krebs J.R. Kacelnik A. Taylor P. Test of optimal sampling by foraging great tits Nature 275 1978 27-31
32. Kristensen S. Sensor planning with Bayesian decision theory Robotics and Autonomous Systems 19 1997 273-286
33. Laird, J. E., & Rosenbloom, P. (1996). The evolution of the SOAR cognitive architecture. In David M. Steier & Tom M. Mitchell (Eds.), Mind matters: A tribute to Allen Newell (pp. 1-50). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
34. Mackintosh, N. J. (1974). The psychology of animal learning. New York: Academic Press.
35. Massaro, D. W. (1997). Perceiving talking faces: From speech perception to a behavioral principle. Cambridge, MA: MIT Press.
36. Massaro D.W. Friedman D. Models of integration given multiple sources of information Psychological Review 97 1990 225-252
37. McCarthy J. Hayes P.J. Some philosophical problems from the standpoint of artificial intelligence Machine Intelligence 4 1969 463-502
38. McFarland, D., & Bosser, T. (1993). Intelligent behavior in animals and robots. Cambridge, MA: MIT Press.
39. Mellers B.A. Schwartz A. Cooke A.D. Judgment and decision making Annual Review in Psychology 49 (1 ) 1998 447-477
40. Mondada, F., & Verschure, P. F. M. J. (1993). Modeling system-environment interaction: The complementary roles of simulations and real world artifacts. In J. L. Deneubourg, H. Bersini, S. Goss, G. Nicolis, & R. Dagonnier (Eds.), Proceedings of the Second European Conference on Artificial Life (pp. 808-817). Cambridge, MA: MIT Press.
41. Nakayama K.Shimojo S.Experiencing and perceiving visual surfaces Science 257 1992 1357-1363
42. Newell A. Physical symbol systems Cognitive Science 4 1980 135-183
43. Newell A. The knowledge level Artificial Intelligence 18 1982 87-127
44. Newell, A. (1990). Unified theories of cognition. Cambridge MA: Harvard University Press.
45. Newell A. Precis of unified theories of cognition Behavioral and Brain Sciences 15 1992 425-492
46. Newell A. Shaw J.C. Simon H.A. A general problem solving program for a computer Computers and Automation 8 1959 10-16
47. Newell, A., & Simon, H. A. (1963). GPS, a program that simulates human thought. In J. Feldman & E. A. Feigenbaum (Eds.), Computers and thought. New York: Mc Graw-Hill.
48. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
49. Newell A. Simon H.A. Computer science as empirical inquiry: Symbols and search Communication of the ACM 19 1976 113-126
50. Pavlov, I. P. (1927). Conditioned reflexes. Oxford: Oxford University Press.
51. Pfeifer R. Cognition: Perspectives from autonomous agents Robotics and Autonomous 15 1995 47-70
52. Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge, MA: MIT Press.
53. Platt M.L. Glimcher P.W. Neural correlates of decision variables in parietal cortex Nature 400 1999 233-238
54. Porrill J. Frisby J.P. Adams W.J. Buckley D. Robust and optimal use of information in stereo vision Nature 397 1999 63-66
55. Rao R. An optimal estimation approach to visual perception and learning Vision Research 3911 1999 1963-1989
56. Rao R.P.N. Ballard D.H. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects Nature Neuroscience 2 1999 79-87
57. Rescorla, R. A. (1980). Pavlovian second-order conditioning: Studies in associate learning. Hillsdale: Erlbaum.
58. Roberts, W. A. (1992). Foraging by rats on a radial maze: Learning, memory, and decision rules. In I. Gormezano & E. A. Wassermann (Eds.), Learning and memory: The behavioural and biological substrates (pp. 7-23). Hillsdale, NJ: Lawrence Erlbaum Associates.
59. Russell, S. J., & Norvig, P. (1995). Artificial intelligence: A modern approach. Englewood Cliffs, NJ: Prentice Hall.
60. Saksida L.M. Raymond S.M. Touretzky D.S. Shaping robot behavior using principles from instrumental conditioning Robotics and Autonomous Systems 22 1997 231-249
61. Sanchez-Montanes M.A. König P. Verschure P.F.M.J. Learning sensory maps with real-world stimuli in real time using a biophysically realistic learning rule IEEE: Transactions on Neural Networks 13 2002 619-632
62. Sanchez-Montanes M.A. Verschure P.F.M.J. König P. Local and global gating of plasticity Neural Computation 12 2000 519-529
63. Schultz W. Dickinson A. Neuronal coding of prediction errors Annual Reviews in Neuroscience 23 2000 473-500
64. Searle J. Minds brains and programs Behavioral and Brain Sciences 3 1982 417-424
65. Simon, H. A. (1969). Sciences of the artificial. Cambridge, MA: MIT Press.
66. Suchman, L. A. (1987). Plans and situated actions. Cambridge: Cambridge University Press.
67. Thorndike, E. L. (1911). Animal intelligence. New York: Macmillan.
68. Thrun S. Bayesian landmark learning for mobile robot localization Machine Learning 33 1998 41-76
69. Touretzky D.S. Saksida L.M. Operant conditioning in Skinnerbots Adaptive Behavior 5 1997 219-247
70. Tversky A. Kahneman D. The framing of decisions and the psychology of choice Science 2114481 1981 453-458
71. Verschure P.F.M.J. Formal minds and biological brains IEEE Expert 8 (5) 1993 66-75
72. Verschure, P. F. M. J. (1997). Xmorph: A software tool for the synthesis and analysis of neural systems (Technical report). Institute of Neuroinformatics, ETH-UZ.
73. Verschure, P. F. M. J., (1998). Synthetic epistemology: The acquisition, retention, and expression of knowledge in natural and synthetic systems. In Proceedings World Conference on Computational Intelligence 1998. Anchorage (pp. 147-153). IEEE.
74. Verschure, P. F. M. J. (2000). The cognitive development of an autonomous behaving artifact: The self-organization of categorization, sequencing, and chunking. In H. Ritter, H. Cruse, & J. Dean (Eds.), Prerational intelligence: Adaptive behavior and intelligent systems without symbols and logic (Vol. 2, pp. 469-488). Dordrecht: Kluwer Academic Publishers.
75. Verschure P.F.M.J. Kröse B. Pfeifer R. Distributed adaptive control: The self-organization of structured behavior Robotics and Autonomous Systems 9 1992 181-196
76. Verschure, P. F. M. J., & Pfeifer, R. (1992). Categorization, representations, and the dynamics of system-environment interaction: A case study in autonomous systems. In J. A. Meyer, H. Roitblat, & S. Wilson (Eds.), From animals to animals: Proceedings of the Second International Conference on Simulation of Adaptive Behavior, Hawaii, Honolulu (pp. 210-217). Cambridge, MA: MIT press.
77. Verschure P.F.M.J. Voegtlin T. A bottom-up approach towards the acquisition, retention, and expression of sequential representations: Distributed adaptive control III Neural Networks 11 1998 1531-1549
78. Verschure P.F.M.J. Wray J. Sporns O. Tononi G. Edelman G.M. Multilevel analysis of classical conditioning in a behaving real world artifact Robotics and Autonomous Systems 16 1995 247-265
79. Vinkhuyzen R.E. Verschure P.F.M.J. The legacy of Allen Newell: Unified theories of cognition American Journal of Psychology 1073 1994 454-464
80. Voegtlin T. Verschure P.F.M.J. What can robots tell us about brains? A synthetic approach towards the study of learning and problem solving Reviews in the Neurosciences 10 (3-4) 1999 291-310
81. Weiss, Y., & Adelson, E. H. (1998). Slow and smooth: A Bayesian theory for the combination of local motion signals in human vision. A.I. Memo 1624, Dept. of Brain and Cognitive Sciences, MIT.