Inducing finite state machines from training samples using ant colony optimization
- 99 Downloads
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.
KeywordsTraining Sample Unmanned Aerial Vehicle System Science International Finite State Machine Continuous Output
Unable to display preview. Download preview PDF.
- 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.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.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.L. A. Gladkov, V. V. Kureichik, and V. M. Kureichik, Genetic Algorithms (Fizmatlit, Moscow, 2006) [in Russian].Google Scholar
- 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
- 9.V. M. Kureichik and S. I. Rodzin, “Evolutionary Algorithms: Genetic Programming,” J. Comput. Syst. Sci. Int. 41, 123–132 (2002).Google Scholar
- 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
- 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).CrossRefzbMATHGoogle Scholar
- 15.M. Dorigo, “Optimization, learning and natural algorithms,” PhD Thesis (Dipartimento di Elettronica, Politechnico di Milano, Milano, 1992).Google Scholar
- 18.FlightGear. http://www.flightgear.org/. Accessed February 14, 2013.