Artificial curiosity based on discovering novel algorithmic predictability through coevolution

Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999

Volumn 3, Issue , 1999, Pages 1612-1618

  Schmidhuber, Jürgen ^a  

^a DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; REINFORCEMENT LEARNING;

CO-EVOLUTION; CO-EVOLUTIONARY; EMBEDDED AGENTS; EVENT SEQUENCE; INTERNAL REPRESENTATION; NOVEL ALGORITHM; RANDOM ALGORITHMS; SPATIO-TEMPORAL DOMAINS;

EVOLUTIONARY ALGORITHMS;

EID: 84901391337     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.1999.785467     Document Type: Conference Paper

References (30)

1. E. B. Baum and I. Durdanovic. Toward code evolution by artificial economies. Technical report, NEC Research Institute, Princeton, NJ, 1998. Extension of a paper in Proc. 13th ICML'1996, Morgan Kaufmann, CA.
2. P. Bourgine and D. Snyers. Mimicry and coevolution of hedonic agents. In Proc. of the third European Conference on Artificial Life, LNAI 929, pages 801-811. Springer, 1995.
3. D. A. Cohn. Neural network exploration using optimal experiment design. In J. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems 6, pages 679-686. San Mateo, CA: Morgan Kaufmann, 1994.
4. V. V. Fedorov. Theory of optimal experiments. Academic Press, 1972.
5. D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, pages 313-324. Addison Wesley Publishing Company, 1992.
6. J. H. Holland. Properties of the bucket brigade. In Proceedings of an International Conference on Genetic Algorithms. Hillsdale, NJ, 1985.
7. J. Hwang, J. Choi, S. Oh, and R. J. Marks II. Querybased learning applied to partially trained multilayer perceptrons. IEEE Transactions on Neural Networks, 2(1):131-136, 1991.
8. D. Lenat. Theory formation by heuristic search. Machine Learning, 21, 1983.
9. D. J. C. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(2):550-604, 1992.
10. M. Plutowski, G. Cottrell, and H. White. Learning Mackey-Glass from 25 examples, plus or minus 2. In J. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems 6, pages 1135-1142. San Mateo, CA: Morgan Kaufmann, 1994.
11. J. B. Pollack and A. D. Blair. Why did TD-Gammon work? In M. C. Mozer, M. I. Jordan, and S. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 10-16. MIT Press, Cambridge, 1997.
12. A. L. Samuel. Some studies in machine learning using the game of checkers. IBM Journal on Research and Development, 3:210-229, 1959.
13. J. Schmidhuber. Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. Institut fur Informatik, Technische Universität München, 1987.
14. J. Schmidhuber. The Neural Bucket Brigade: A local learning algorithm for dynamic feedforward and recurrent networks. Connection Science, 1(4):403-412, 1989.
15. J. Schmidhuber. Curious model-building control systems. In Proc. International Joint Conference on Neural Networks, Singapore, volume 2, pages 1458-1463. IEEE, 1991.
16. J. Schmidhuber. A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991.
17. J. Schmidhuber. What's interesting? Technical Report IDSIA-35-97, IDSIA, 1997. Extended abstract in Proc. Snowbird'98, Utah, 1998. ftp://ftp.idsia.ch/ pub/juergen/interest.ps.gz
18. J. Schmidhuber. A general method for incremental self-improvement and multi-agent learning. In X. Yao, editor, Evolutionary Computation: Theory and Applications, pages 81-123. Scientific Publ. Co., Singapore, 1999.
19. J. Schmidhuber and D. Prelinger. Discovering predictable classifications. Neural Computation, 5(4):625-635, 1993.
20. J. Schmidhuber, J. Zhao, and N. Schraudolph. Reinforcement learning with self-modifying policies. In S. Thrun and L. Pratt, editors, Learning to learn, pages 293-309. Kluwer, 1997.
21. J. Schmidhuber, J. Zhao, and M. Wiering. Shifting inductive bias with success-story algorithm, adaptive Levin Search, and incremental self-improvement. Machine Learning, 28:105-130, 1997.
22. C. E. Shannon. A mathematical theory of communication (parts I and II). Bell System Technical Journal, XXVII: 379-423, 1948.
23. J. Storck, S. Hochreiter, and J. Schmidhuber. Reinforcement driven information acquisition in non-deterministic environments. In Proceedings of the International Conference on Artificial Neural Networks, Paris, volume 2, pages 159-164. EC2 & Cie, Paris, 1995.
24. G. Tesauro. TD-gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation, 6(2):215-219, 1994.
25. S. Thrun and K. Moller. Active exploration in dynamic environments. In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 4, pages 531-538. San Mateo, CA: Morgan Kaufmann, 1992.
26. G. Weiss. Hierarchical chunking in classifier systems. In Proceedings of the 12th National Conference on Artificial Intelligence, volume 2, pages 1335-1340. AAAI Press/The MIT Press, 1994.
27. G. Weiss and S. Sen, editors. Adaption and Learning in Multi-Agent Systems. LNAI 1042, Springer, 1996.
28. S. W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2:1-18, 1994.
29. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
30. D. H. Wolpert, K. Turner, and J. Frank. Using collective intelligence to route internet traffic. In M. Kearns, S. A. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 12. MIT Press, Cambridge MA, 1999.