Sequential Behavior and Learning in Evolved Dynamical Neural Networks

Adaptive Behavior

Volumn 2, Issue 3, 1994, Pages 219-246

  Yamauchi, Brian M ^a   Beer, Randall D ^a  

^a CASE WESTERN RESERVE UNIVERSITY   (United States)

Author keywords

genetic algorithms; neural networks; sequential behavior; reinforcement learning

Indexed keywords

EID: 84976922520     PISSN: 10597123     EISSN: 17412633     Source Type: Journal    
DOI: 10.1177/105971239400200301     Document Type: Article

References (23)

1. Abraham, R.H., & Shaw, C.D. (1992). Dynamics—The geometry of behavior (2nd ed.). Redwood City, CA: Addison-Wesley.
2. Beer, R.D. (in press, a). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence.
3. Beer, R.D. (in press, b). Computational and dynamical languages for autonomous agents. In T. van Gelder & R. Port (Eds.), Mind as motion. Cambridge, MA: MIT Press.
4. Beer, R.D., & Gallagher, J.C. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1, 91–122.
5. Belew, R., McInerney, J., & Schraudolph, N.N. (1990). Evolving networks: Using the genetic algorithm with connectionist learning (CSE Tech. Rep. CS90–174). San Diego: University of California.
6. Chalmers, D.J. (1991). The evolution of learning: An experiment in genetic connectionism. In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, & G. E. Hinton (Eds.), Connectionist models: Proceedings of the 1990 summer school. San Mateo, CA: Morgan Kaufmann.
7. Chapman, D., & Kaelbling, L.P. (1991). Input generalization in delayed reinforcement learning: An algorithm and performance comparisons. In Proceedings of the International Joint Conference on Artificial Intelligence [IJCAI-91]. San Mateo, CA: Morgan Kaufmann.
8. Cleeremans, A., Servan-Schreiber, D., & McClelland, J.L. (1989). Finite state automata and simple recurrent networks. Neural Computation 1(3), 372–381.
9. Cliff, D., Harvey, I., & Husbands, P. (1993). Explorations in evolutionary robotics. Adaptive Behavior 2(1), 73–110.
10. Garcia, J., Hankins, W.G., & Rusiniak, K.W. (1974). Behavioral regulation of the milieu interne in man and rat. Science 185, 824–831.
11. Giles, C.L., Miller, C.B., Chen, D., Chen, H.H., Sun, G.Z., & Lee, Y.C. (1992). Learning and extracting finite state automata with second-order recurrent neural networks. Neural Computation 4(3), 393–405.
12. Hale, J.K., & Koçak, H. (1991). Dynamics and bifurcations. New York: Springer-Verlag.
13. Hinton, G.E., & Nowlan, S.J. (1987). How learning can guide evolution. Complex Systems 1, 495–502.
14. Kaelbling, L.P. (1993). Learning in embedded systems. Cambridge, MA: MIT Press.
15. Maes, P., & Brooks, R. (1990). Learning to coordinate behaviors. In Proceedings of the Eighth National Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann.
16. Millan, J. del., & Torres, C. (1992). A reinforcement connectionist approach to robot path finding in non-maze-like environments. Machine Learning 8, 363–395.
17. Miller, G.F., & Todd, P.M. (1991). Exploring adaptive agency: I. Theory and methods for simulating the evolution of learning. In D. S. Touretzky, J. L. Elman, T.J. Sejnowski, & G. E. Hinton (Eds.), Connectionist models: Proceedings of the 1990 summer school. San Mateo, CA: Morgan Kaufmann.
18. Pollack, J.B. (1991). The induction of dynamical recognizers. Machine Learning 7, 227–252.
19. Sutton, R.S. (1988). Learning to predict by the method of temporal differences. Machine Learning 3(1), 9–44.
20. Watkins, C.J.C.H. (1989). Learning from delayed rewards. Unpublished doctoral thesis, University of Cambridge, England.
21. Watrous, R.L., & Kuhn, G.M. (1992). Induction of finite-state languages using second-order recurrent networks. Neural Computation 4(3), 406–414.
22. Wiggins, S. (1990). Introduction to applied nonlinear dynamical systems and chaos. New York: Springer-Verlag.
23. Yamauchi, B.M. (1993). Dynamical neural networks for mobile robot control (NRL Memorandum Report AIC-033–93). Washington, DC: Naval Research Laboratory.