умови невизначеності

Досліджено проблему автоматизованого керування в умовах невизначеності математичної моделі системи. Запропоновано структуру системи керування на основі моделі інтелектуального агента з нечіткою логікою. Вироблення керуючих дій виконано з урахуванням експертних знань про систему, сформульованих у вигляді правил нечіткого логічного виведення рішень. Розроблено алгоритмічне та програмне забез- печення системи керування з нечіткою логікою.

The game model of multi-agent systems of self-organizing in the conditions of uncertainty is developed. The formulation of a stochastic game problem is carried out, criteria of self-organizing of strategies of players are defined, a recurrent method, algorithm and software of learning of multi-agent system to simulate the synchronised rhythmic luminescence of a colony of fireflies are developed.

The neuroagent game model of collective decision-making in the conditions of uncertainty is developed. The formulation of stochastic game is executed. Adaptive learning methods of artificial neural networks without the teacher are used for the game solving. The convergence of neuroagent stochastic game is confirmed by results of computer experiment. Influences of parameters of game model on the neuroagent learning rate are investigated.

The adaptive game method of events synchronization in multiagent systems in the conditions of uncertainty is developed. The essence of a method consists in alignment of delays of approach of events on the basis of supervision of actions of the next players. The formulation of stochastic game is executed and game algorithm for its solving is developed. Influences of parameters on convergence of a game method are investigated by means of computer experiment.

The model of matrix stochastic game for decision-making in the conditions of uncertainty is developed. The method of Q-learning for stochastic game solving with a priori unknown gains matrices is offered. The formulation of a game problem is executed. The Markovian recurrent method and algorithm for the game solving are described. Results of computer modelling of stochastic game with Q-learning are received and analysed.