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

DOI10.1134/S1064230715050044zbMath1383.93057OpenAlexW2209342067MaRDI QIDQ1742411

V. I. Ulyantsev, S. V. Kazakov, A. A. Shalyto, I. P. Buzhinsky, F. N. Tsarev

Publication date: 11 April 2018

Published in: Journal of Computer and Systems Sciences International (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s1064230715050044

zbMATH Keywords

complex behaviorfinite-state machinestraining examplesreliable control systems

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Application models in control theory (93C95) Design techniques (robust design, computer-aided design, etc.) (93B51) Dynamical aspects of cellular automata (37B15)

Uses Software

• irace
• FlightGear

Cites Work

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• The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior
• A genetic algorithm for finding a salesman's route
• Method of reduced tables for generation of automata with a large number of input variables based on genetic programming
• Inducing finite state machines from training samples using ant colony optimization
• Genetic algorithms: state of the art, problems, and perspectives

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