Learning Finite-State Machines with Ant Colony Optimization

  • Daniil Chivilikhin
  • Vladimir Ulyantsev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7461)

Abstract

In this paper we present a new method of learning Finite- State Machines (FSM) with the specified value of a given fitness function, which is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. The input data is a set of events, a set of actions and the number of states in the target FSM and the goal is to maximize the given fitness function, which is defined on the set of all FSMs with given parameters. Comparison of the new algorithm and a genetic algorithm (GA) on benchmark problems shows that the new algorithm either outperforms GA or works just as well.

References

  1. 1.
    Spears, W.M., Gordon, D.E.: Evolving finite-state machine strategies for protecting resources. In: Proceedings of the International Symposium on Methodologies for Intelligeng Systems, pp. 166–175 (2000)Google Scholar
  2. 2.
    Lucas, S., Reynolds, J.: Learning dfa: Evolution versus evidence driven state merging. In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 1, pp. 351–348 (2003)Google Scholar
  3. 3.
    Polykarpova, N., Shalyto, A.: Automata-based programming. Piter (2009) (in Russian)Google Scholar
  4. 4.
    Tsarev, F., Egorov, K.: Finite-state machine induction using genetic algorithm based on testing and model checking. In: Proceedings of the 2011 GECCO Conference Companion on Genetic and Evolutionary Computation (GECCO 2011), pp. 759–762 (2011), http://doi.acm.org/10.1145/2001858.2002085, doi:10.1145/2001858.2002085
  5. 5.
    Tsarev, F.: Method of finite-state machine induction from tests with genetic programming. Information and Control Systems (Informatsionno-upravljayushiye sistemy, in Russian) (5), 31–36 (2010)Google Scholar
  6. 6.
    Tsarev, F., Shalyto, A.: Use of genetic programming for finite-state machine generation in the smart ant problem. In: Proceedings of the IV International Scientific-Practical Conference ”Integrated Models and Soft Calculations in Artificial Intelligence”, vol. (2), pp. 590–597 (2007)Google Scholar
  7. 7.
    Alba, E., Chicano, F.: Acohg: dealing with huge graphs. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computing (GECCO 2007), pp. 10–17 (2007), http://doi.acm.org/10.1145/1276958.1276961, doi:10.1145/1276958.1276961
  8. 8.
    Koza, J.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniil Chivilikhin
    • 1
  • Vladimir Ulyantsev
    • 1
  1. 1.Mechanics and OpticsSaint-Petersburg National Research University of Information TechnologiesSaint-PetersburgRussia

Personalised recommendations