Inducing finite state machines from training samples using ant colony optimization

  • I. P. Buzhinsky
  • V. I. Ulyantsev
  • D. S. Chivilikhin
  • A. A. Shalyto
Artificial Intelligence

Abstract

A method for control finite state machine (FSM) induction in which an ant colony optimization algorithm is used for search optimization is proposed. The efficiency of this method is estimated using the generation of FSMs for controlling a model of an unmanned aerial vehicle (UAV). It is shown that the proposed method outperforms the method based on genetic algorithms both in terms of performance and quality.

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References

  1. 1.
    N. I. Polikarpova and A. A. Shalyto, Automata-Based Programming (Piter, St. Petersburg, 1991) [in Russian], http://is.ifmo.ru/books/-book.pdf Google Scholar
  2. 2.
    V. O. Kleban and A. A. Shalyto, “Development of control system for a small-size helicopter,” Nauchno-tekhnicheskii Vestn. St. Petersburg Gos. Univ. Inform. Tekhnologii, Mekhaniki i Optiki, No. 2, 12–16 (2011). http://is.ifmo.ru/works/2011/Vestnik/72-2/02-Kleban-Shalyto.pdf Google Scholar
  3. 3.
    F. N. Tsarev and A. A. Shalyto, “The use of genetic programming for generating a finite state machine in the smart ant problem,” in Proc. of the 4th Int. Conf. on Integrated Models and Soft Computations in Artificial Intelligence (Fizmatlit, Moscow, 2007), pp. 590–597. http://is.ifmo.ru/genalg/-ant-ga.pdf
  4. 4.
    F. N. Tsarev, “Combined use of genetic programming, finite state machines, and artificial neural networks for designing a control system for an unmanned aerial vehicle,” Nauchno-tekhnicheskii Vestn. St. Petersburg Gos. Univ. Inform. Tekhnologii, Mekhaniki i Optiki, No. 2, 12–16 (2011). http://is.ifmo.ru/works/2008/Vestnik/53/03-genetic-neuro-automata-flying-plates.pdf Google Scholar
  5. 5.
    L. A. Gladkov, V. V. Kureichik, and V. M. Kureichik, Genetic Algorithms (Fizmatlit, Moscow, 2006) [in Russian].Google Scholar
  6. 6.
    S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, Upper Saddle River, N.J., 2003; Vil’yams, Moscow, 2003).Google Scholar
  7. 7.
    J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, (1992).MATHGoogle Scholar
  8. 8.
    V. M. Kureichik, “Genetic Algorithms: State of the Art, Problems, and Perspectives,” J. Comput. Syst. Sci. Int. 38, 137–152 (1999).MATHGoogle Scholar
  9. 9.
    V. M. Kureichik and S. I. Rodzin, “Evolutionary Algorithms: Genetic Programming,” J. Comput. Syst. Sci. Int. 41, 123–132 (2002).Google Scholar
  10. 10.
    M. Heule and S. Verwer, “Exact DFA identification using SAT solvers,” in Grammatical Inference: Theoretical Results and Applications, 10th Int. Colloquium (ICCGI 2010), Lect. Notes Comput. Sci. 6339, pp. 66–79 (2012).CrossRefGoogle Scholar
  11. 11.
    V. Ulyantsev and F. Tsarev, “Extended finite-state machine induction using SAT-solver,” in Proc. of the 14th IFAC Symp. on Information Control Problems in Manufacturing (INCOM’12), 2012, pp. 512–517.Google Scholar
  12. 12.
    T. H. Cormen, C. E. Leiserson, and R. L. Rivest, Introduction to Algorithms (MIT Press, Cambridge, Mass., 1990; Vil’yams, Moscow, 1999).MATHGoogle Scholar
  13. 13.
    N. I. Polikarpova, V. N. Tochilin, and A. A. Shalyto, “Method of reduced tables for generation of automata with a large number of input variables based on genetic programming,” J. Comput. Syst. Sci. Int. 49, 265–282 (2010).CrossRefGoogle Scholar
  14. 14.
    A. V. Aleksandrov, S. V. Kazakov, A. A. Sergushichev, F. N. Tsarev, and A. A. Shalyto, “The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior,” J. Comput. Syst. Sci. Int. 52, 410–425 (2013).CrossRefMATHGoogle Scholar
  15. 15.
    M. Dorigo, “Optimization, learning and natural algorithms,” PhD Thesis (Dipartimento di Elettronica, Politechnico di Milano, Milano, 1992).Google Scholar
  16. 16.
    M. Dorigo and T. Stützle, Ant Colony Optimization (MIT Press, Cambridge, Mass., 2004).CrossRefMATHGoogle Scholar
  17. 17.
    D. Chivilikhin and V. Ulyantsev, “Learning finite-state machines with ant colony optimization,” Lect. Notes Comput. Sci. 7461, 268–275 (2012).CrossRefGoogle Scholar
  18. 18.
    FlightGear. http://www.flightgear.org/. Accessed February 14, 2013.

Copyright information

© Pleiades Publishing, Ltd. 2014

Authors and Affiliations

  • I. P. Buzhinsky
    • 1
  • V. I. Ulyantsev
    • 1
  • D. S. Chivilikhin
    • 1
  • A. A. Shalyto
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

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