Advertisement

Evolution of Strategies for Resource Protection Problems

  • William M. Spears
  • Diana F. Gordon-Spears
Part of the Natural Computing Series book series (NCS)

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.

Keywords

Model Check Good Individual Board Size Accessible State Cycle Repair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bäck, T. and Schwefel, H.-P. (1993) An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1, 1–23CrossRefGoogle Scholar
  2. 2.
    Billings, L., Spears, W., and Schwartz, I. (2002) A unified prediction of computer virus spread in connected networks. Physics Letters A, 297, 261–266MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Carmel, D. and Markovitch, S. (1996) Learning models of intelligent agents. Proceedings of the Thirteenth National Conference on Artificial IntelligenceGoogle Scholar
  4. 4.
    Clarke, E. and Wing, J. (1996) Formal methods: State of the art and future directions. ACM Computing Surveys, 28, 626–643CrossRefGoogle Scholar
  5. 5.
    Dean, T. and Wellman, M. (1991) Planning and Control. Morgan Kaufmann, San MateoGoogle Scholar
  6. 6.
    De Jong, K., Spears, W., and Gordon, D. (1993) Using genetic algorithms for concept learning. Machine Learning Journal, 13, 161–188CrossRefGoogle Scholar
  7. 7.
    Denning, D. (1999) Information Warfare and Security. Addison-Wesley, New YorkGoogle Scholar
  8. 8.
    Fogel, D. (1995) Evolutionary Computation. IEEE Press, New YorkGoogle Scholar
  9. 9.
    Fogel, L. (1999) Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming. Wiley Series on Intelligent Systems, New YorkzbMATHGoogle Scholar
  10. 10.
    Fogel, L., Owens, A., and Walsh, M. (1966) Artificial Intelligence Through Simulated Evolution. John Wiley and Sons, New YorkzbMATHGoogle Scholar
  11. 11.
    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 SystemsGoogle Scholar
  12. 12.
    Grefenstette, J. and Fitzpatrick, J. (1985) Genetic search with approximate function evaluations. Proceedings of the International Conference on Genetic AlgorithmsGoogle Scholar
  13. 13.
    Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann ArborGoogle Scholar
  14. 14.
    Hopcroft, J. and Ullman, J. (1979) Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, Menlo ParkzbMATHGoogle Scholar
  15. 15.
    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 IIGoogle Scholar
  16. 16.
    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 SystemsGoogle Scholar
  17. 17.
    Lamarck, J.B. (1984) Philosophie Zoologique. English Translation, University of ChicagoGoogle Scholar
  18. 18.
    Mars, P., Chen, J., and Nambiar, R. (1996) Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications. CRC Press, New YorkGoogle Scholar
  19. 19.
    Rich, E. and Knight, K. (1991) Artificial Intelligence. McGraw-Hill, New YorkGoogle Scholar
  20. 20.
    Spears, W. (2000) Evolutionary Algorithms: The Role of Mutation and Recombination. Springer-Verlag, BerlinzbMATHCrossRefGoogle Scholar
  21. 21.
    Spears, W. and De Jong, K. (1991) On the virtues of parameterized uniform crossover. Proceedings of the International Conference on Genetic AlgorithmsGoogle Scholar
  22. 22.
    Watkins, C. (1989) Learning from delayed rewards. Ph.D. thesis, University of Cambridge, EnglandGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • William M. Spears
    • 1
  • Diana F. Gordon-Spears
    • 1
  1. 1.Department of Computer ScienceCollege of Engineering University of WyomingLaramieUSA

Personalised recommendations