Modification of the method of generation of control finite-state machines with continuous actions based on training examples

  • I. P. Buzhinsky
  • S. V. Kazakov
  • V. I. Ulyantsev
  • F. N. Tsarev
  • A. A. Shalyto
Discrete Systems


Control finite-state machines can be used in the development of reliable control systems due to their clarity and because it is possible to formally verify them. The paper deals with resolving the problem of the generation of machines that control plants with complex behavior based on training examples. The input and output actions of the machines are given by real numbers. A method for the generation of machines is proposed. It is a modification of the previously proposed approaches based on the genetic and ant colony optimization algorithms. Changes include a new way of representing machines and improving the fitness function. The method makes it possible to generate machines whose behavior is more consistent with training examples than the behavior of machines generated by the known approaches.


Control Parameter Fitness Function Pitch Angle Output Action System Science International 
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.


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  1. 1.
    D. Harel and A. Pnueli, “On the development of reactive systems,” in Logic and Models of Concurrent Systems, Ed. by K. Apt, NATO Advanced Study Institute on Logic and Models for Verification and Specification of Concurrent Systems Series (Springer-Verlag, 1985), pp. 477–498.CrossRefGoogle Scholar
  2. 2.
    D. Harel and M. Politi, Modeling Reactive Systems with Statechart. The Statemate Approach (McGraw-Hill, New York, 1998).Google Scholar
  3. 3.
    N. I. Polikarpova and A. A. Shalyto, Automata-Based Programming (Piter, St.-Petersburg, 2011) [in Russian]. Scholar
  4. 4.
    N. Walkinshaw, K. Bogdanov, M. Holcombe, et al., “Reverse engineering state machines by interactive grammar inference,” in Proceedings of the 14th Working Conference on Reverse Engineering, WCRE’07 (IEEE Computer Society Press, Vancouver, 2007), pp. 209–218.Google Scholar
  5. 5.
    N. Walkinshaw, R. Taylor, and J. Derrick, “Inferring extended finite state machine models from software executions,” in Proceedings of the 20th Working Conference on Reverse Engineering WCRE’13 (IEEE Computer Society Press, Koblenz, 2013), pp. 301–310.Google Scholar
  6. 6.
    F. N. Tsarev and A. A. Shalyto, “Application of genetic programming for automata generation in the ‘artificial ant’ problem,” in Proceedings of the 4th International Scientific-Practical Conference on Integrated Models and Soft Computing in Artificial Intelligence (Fizmatlit, Moscow, 2007), Vol. 2, pp. 590–597. Scholar
  7. 7.
    J. R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, 1992).zbMATHGoogle Scholar
  8. 8.
    V. M. Kureichik, “Genetic algorithms: state of the art, problems, and perspectives,” J. Comput. Syst. Sci. Int. 38, 137 (1999).zbMATHGoogle Scholar
  9. 9.
    V. M. Kureichik and S. I. Rodzin, “Evolutionary algorithms: genetic programming,” J. Comput. Syst. Sci. Int. 41, 123 (2002).Google Scholar
  10. 10.
    L. A. Gladkov, V. V. Kureichik, and V. M. Kureichik, Genetic Algorithms (Fizmatlit, Moscow, 2006) [in Russian].Google Scholar
  11. 11.
    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 (2010).CrossRefzbMATHGoogle Scholar
  12. 12.
    F. N. Tsarev, “Induction of finite state machines using genetic programming with fitness based on testing,” Inform.-Upravl. Sist., No. 5, 31–36 (2010).Google Scholar
  13. 13.
    A. V. Aleksandrov, S. V. Kazakov, A. A. Sergushichev, et al., “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 (2013).CrossRefzbMATHGoogle Scholar
  14. 14.
    I. Buzhinsky, V. Ulyantsev, and A. Shalyto, “Test-based induction of finite-state machines with continuous output actions,” in Proceedings of the 7th IFAC Conference on Manufacturing Modelling, Management, and Control MIM’13 (IFAC, St.-Petersburg, 2013), pp. 1049–1054.Google Scholar
  15. 15.
    I. P. Buzhinsky, V. I. Ulyantsev, D. S. Chivilikhin, and A. A. Shalyto, “Inducing finite state machines from training samples using ant colony optimization,” J. Comput. Syst. Sci. Int. 53, 256 (2014).CrossRefzbMATHGoogle Scholar
  16. 16.
    FlightGear. Cited September 29, 2014.Google Scholar
  17. 17.
    M. Dorigo and T. Stützle, Ant Colony Optimization (MIT Press, US, 2004).CrossRefzbMATHGoogle Scholar
  18. 18.
    D. Chivilikhin and V. Ulyantsev, “Learning finite-state machines with ant colony optimization,” Lect. Notes Comp. Sci. 7461, 268–275 (2012).CrossRefGoogle Scholar
  19. 19.
    M. López-Ibáñez, J. Dubois-Lacoste, T. Stützle, et al., “The irace package, iterated race for automatic algorithm configuration,” Tech. Report TR/IRIDIA/2011-004 (IRIDIA, Univ. libre de Bruxelles, Belgium, 2011).Google Scholar
  20. 20.
    Transas. Cited September 29, 2014.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  • I. P. Buzhinsky
    • 1
  • S. V. Kazakov
    • 1
  • V. I. Ulyantsev
    • 1
  • F. N. Tsarev
    • 1
  • A. A. Shalyto
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

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