Method of reduced tables for generation of automata with a large number of input variables based on genetic programming

  • N. I. Polikarpova
  • V. N. Tochilin
  • A. A. Shalyto
Artificial Intelligence

Abstract

Known methods of automatic generation of finite automata based on genetic programming are inefficient in the case of a large number of input variables of the automaton. A method free from this disadvantage is proposed. The preference of this method for a large number of input variables is theoretically substantiated and experimentally proved. The method was used for automation of development of an aircraft control system on a high level of abstraction.

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References

  1. 1.
    V. M. Kureichik, “Genetic Algorithms: State of the Art, Problems, and Perspectives”, Izv. Ross. Akad. Nauk, Izv. Ross. Akad. Nauk, Teor. Sist. Upr., No. 1, 144–160 (1999) [Comp. Syst. Sci. 38 (1), 137-152 (1999)].Google Scholar
  2. 2.
    V.V. Kureichik, V.M. Kureichik, and P.V. Sorokoletov, “Analysis and a Survey of Evolutionary Models”, Izv. Ross. Akad. Nauk, Izv. Ross. Akad. Nauk, Teor. Sist. Upr., No. 5, 114–126 (2007) [Comp. Syst. Sci. 46 (5), 779—791 (2007)].Google Scholar
  3. 3.
    J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, 1992).Google Scholar
  4. 4.
    V. M. Kureichik and S. I. Rodzin, “Evolutionary Algorithms: Genetic Programming”, Izv. Ross. Akad. Nauk, Izv. Ross. Akad. Nauk, Teor. Sist. Upr., No. 1, 127–137 (2002) [Comp. Syst. Sci. 41 (1), 123–132 (2002)].Google Scholar
  5. 5.
    E. M. Gold, “Language Identification in the Limit”, Informat. Control, 10, 447–474(1967).MATHCrossRefGoogle Scholar
  6. 6.
    A. Belz, “Computational Learning of Finite-State Models for Natural Language Processing”, PhD thesis, University of Sussex, 2000.Google Scholar
  7. 7.
    C. H. Clelland and D. A. Newlands, “Pfsa Modelling of Behavioural Sequences by Evolutionary Programming”, in Proceedings of 2nd Australian Conf. on Complex Systems Complex’94, Rockhampton, Queensland, Australia, 1994, 165–172.Google Scholar
  8. 8.
    S. Das and M.C. Mozer, “A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction”, Advances in Neural Information Processing Systems, 6 (1994).Google Scholar
  9. 9.
    M. M. Lankhorst, “A Genetic Algorithm for the Induction of Nondeterministic Pushdown Automata”, Computing Science Report (University of Croningen Department of Computing Science, Groningen, 1995).Google Scholar
  10. 10.
    A. Belz and B. Eskikaya, “A Genetic Algorithm for Finite State Automata Induction with an Application to Phonotactics”, in Proceedings of ESSLLI-98 Workshop on Automated Acquisition of Syntax and Parsing, Saar-bruecken, Germany, 1998, 9–17.Google Scholar
  11. 11.
    D. Ashlock, A. Wittrock, and T.-J. Wen, “Training Finite State Machines to Improve PCR Primer Design”, in Proceedings of Congress on Evolutionary Computation (CEC’02), Honolulu, Hawaii, USA, 2002, 13–18.Google Scholar
  12. 12.
    D. A. Ashlock, S. J. Emrich, K. M. Bryden, et al., “A Comparison of Evolved Finite State Classifiers and Interpolated Markov Models for Improving PCR Primer Design”, in Proceedings of 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’04), Jolla, California, USA, 2004, 190–197.Google Scholar
  13. 13.
    S. M. Lucas, “Evolving Finite State Transducers: Some Initial Explorations”, in Proceedings of Genetic Programming: 6th European Conference (EuroGPKh03) (Springer, Berlin, 2003), 130–141.Google Scholar
  14. 14.
    P. G. Lobanov and A. A. Shalyto, “Application of Genetic Algorithms for Automatic Construction of Finite-State Automata in the Problem of Flibs”, Izv. Ross. Akad. Nauk, Izv. Ross. Akad. Nauk, Teor. Sist. Upr., No. 5, 127–136 (2007) [Comp. Syst. Sci. 46 (5), 792-801 (2007)].Google Scholar
  15. 15.
    F. N. Tsarev and A. A. Shalyto, “Application of Genetic Algorithms for Construction of Automatons with Minimal Number of States for the “Smart Ant” Problem”, in Scientific Software in Education and Scientific Research (SPbGU PU, St. Petersburg, 2008), 209–215.Google Scholar
  16. 16.
    A. Teller, and M. Veloso, PADO: A New Learning Architecture for Object Recognition. Symbolic Visual Learning (Oxford Univ. Press, New York, 1996).Google Scholar
  17. 17.
    W. Banzhaf, P. Nordin, R. E. Keller, et al., Genetic Programming — An Introduction. On the Automatic Evolution of Computer Programs and its Application (Morgan Kaufmann Publishers, San Francisco, 1998).Google Scholar
  18. 18.
    W. Kantschik, P. Dittrich, and M. Brameier, “Empirical Analysis of Different Levels of Meta-Evolution”, in Proceedings of Congress on Evolutionary Computation,, Washington DC, USA, 1999.Google Scholar
  19. 19.
    W. Kantschik, P. Dittrich, M. Brameier, et al., “Meta-Evolution in Graph GP”, in Proceedings of Genetic Programming: Second European Workshop (EuroGP’99), Goeteborg, Sweden, 1999.Google Scholar
  20. 20.
    A. Teller and M. Veloso, “Internal Reinforcement in a Connectionist Genetic Programming Approach”, in Artificial Intelligence (North-Holland Pub. Co, 1970), 161.Google Scholar
  21. 21.
    J. H. Miller, “The Coevolution of Automata in the Repeated Prisoner’s Dilemma. Working Paper” (Santa Fe Institute, 1989).Google Scholar
  22. 22.
    W. M. Spears and D. F. Gordon, “Evolving Finite-State Machine Strategies for Protecting Resources”, in Proceedings of International Syposium on Methodologies for Intelligent Systems, Charlotte, North Carolina, USA, 2000.Google Scholar
  23. 23.
    D. Ashlock, Evolutionary Computation for Modeling and Optimization (Springer, New York, 2006).MATHGoogle Scholar
  24. 24.
    C. Frey and G. Leugering, “Evolving Strategies for Global Optimization. A Finite State Machine Approach”, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, USA, 2001), 27–33.Google Scholar
  25. 25.
    P. Petrovic, “Simulated Evolution of Distributed FSA Behaviour-Based Arbitration”, in Proceedings of Eighth Scandinavian Conference on Artificial Intelligence (SCAI’03), Bergen, Norway, 2003.Google Scholar
  26. 26.
    P. Petrovic, Evolving Automatons for Distributed Behavior Arbitration. Technical Report (Norwegian University of Science and Technology, Trondheim, Norway, 2005).Google Scholar
  27. 27.
    P. Petrovic, Comparing Finite-State Automata Representation with GP-trees. Technical report (Norwegian University of Science and Technology, Trondheim, Norway, 2006).Google Scholar
  28. 28.
    N. I. Polikarpova and A. A. Shalyto, Automaton Programming (Piter, St. Petersburg, 2009) [in Russian].Google Scholar
  29. 29.
    J.R. Koza, Future Work and Practical Applications of Genetic Programming. Handbook of Evolutionary Computation (IOP Publishing Ltd, Bristol, 1997).Google Scholar
  30. 30.
    N. I. Polikarpova, V. N. Tochilin, and A. A. Shalyto, “Application of Genetic Programming for Implementation of Systems with Complex Behavior”, in Proceedings of 4th International Scientific-Practical Conference on Integrated Models and Soft Calculations in Artificial Intelligence, vol. 2 (Fizmatlit, Moscow, 2007), 598–604.Google Scholar
  31. 31.
    N. I. Polikarpova, V. N. Tochilin, and A. A. Shalyto, “Development of a Library for Generation of Control Automata Using Genetic Programming”, in Proceedings of 10th International Conference on Soft Calculations and Measurements, vol. 2 (SPbGETU “LETI”, St. Petersburg, 2007).Google Scholar
  32. 32.
  33. 33.
    E. Hull, K. Jackson, and J. Dick, Requirements Engineering (Springer, Berlin, 2002).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2010

Authors and Affiliations

  • N. I. Polikarpova
    • 1
  • V. N. Tochilin
    • 1
  • A. A. Shalyto
    • 1
  1. 1.Mechanics and OpticsSt. Petersburg State University of Information TechnologiesSt. PetersburgRussia

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