Abstract
The objective of this project is to develop effective finite-state machine (FSM) strategies for winning against an adversary in a Competition for Resources simulation. To achieve this goal, we evolve these strategies in a simulated environment and compare a variety of evolutionary methods in this context. Key empirical questions are addressed, such as how many FSM states are optimal, how effective is it to use an evolutionary algorithm that adapts the number of states, and how can one reduce the variance in fitness evaluation? Some of our experimental answers to these questions are quite intriguing. This chapter also explores and evaluates novel algorithms for detecting and repairing deleterious cycles in the evolved FSMs.
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References
Bäck, T. and Schwefel, H.-P. (1993) An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1, 1–23
Billings, L., Spears, W., and Schwartz, I. (2002) A unified prediction of computer virus spread in connected networks. Physics Letters A, 297, 261–266
Carmel, D. and Markovitch, S. (1996) Learning models of intelligent agents. Proceedings of the Thirteenth National Conference on Artificial Intelligence
Clarke, E. and Wing, J. (1996) Formal methods: State of the art and future directions. ACM Computing Surveys, 28, 626–643
Dean, T. and Wellman, M. (1991) Planning and Control. Morgan Kaufmann, San Mateo
De Jong, K., Spears, W., and Gordon, D. (1993) Using genetic algorithms for concept learning. Machine Learning Journal, 13, 161–188
Denning, D. (1999) Information Warfare and Security. Addison-Wesley, New York
Fogel, D. (1995) Evolutionary Computation. IEEE Press, New York
Fogel, L. (1999) Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming. Wiley Series on Intelligent Systems, New York
Fogel, L., Owens, A., and Walsh, M. (1966) Artificial Intelligence Through Simulated Evolution. John Wiley and Sons, New York
Gordon, D., Spears, W., Sokolsky, O., and Lee, I. (1999) Distributed spatial control, global monitoring and steering of mobile physical agents. Proceedings of the IEEE International Conference on Information, Intelligence, and Systems
Grefenstette, J. and Fitzpatrick, J. (1985) Genetic search with approximate function evaluations. Proceedings of the International Conference on Genetic Algorithms
Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor
Hopcroft, J. and Ullman, J. (1979) Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, Menlo Park
Jefferson, D., Collins, R., Cooper, C., Dyer, M., Flowers, M., Korf, R., Taylor, C., and Wang, A. (1991) Evolution as a theme in artificial life: The Genesys/Tracker system. Proceedings of Artificial Life II
Kim, M., Viswanathan, M., Ben-Abdallah, H., Kannan, S., Lee, I., and Sokolsky, O. (1999) Formally specified monitoring of temporal properties. Proceedings of the Euromicro Conference on Real-Time Systems
Lamarck, J.B. (1984) Philosophie Zoologique. English Translation, University of Chicago
Mars, P., Chen, J., and Nambiar, R. (1996) Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications. CRC Press, New York
Rich, E. and Knight, K. (1991) Artificial Intelligence. McGraw-Hill, New York
Spears, W. (2000) Evolutionary Algorithms: The Role of Mutation and Recombination. Springer-Verlag, Berlin
Spears, W. and De Jong, K. (1991) On the virtues of parameterized uniform crossover. Proceedings of the International Conference on Genetic Algorithms
Watkins, C. (1989) Learning from delayed rewards. Ph.D. thesis, University of Cambridge, England
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Spears, W.M., Gordon-Spears, D.F. (2003). Evolution of Strategies for Resource Protection Problems. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_14
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DOI: https://doi.org/10.1007/978-3-642-18965-4_14
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