ПРИМЕНЕНИЕ ВЕРОЯТНОСТНЫХ И ВРЕМЕННЫХ АВТОМАТОВ В ПРОГРАММАХ УПРАВЛЕНИЯ МНОГОАГЕНТНЫХ СИСТЕМ

Abstract

Classical models of the time and probabilistic automaton constructed using the theory of Markov chains - the system "automaton-random environment" described by the Markov chain are considered. The uncertainty of the probabilistic automaton can be determined by various reasons, including the nondeterminism of the rules of state change of the simulated system, which is one of the most important obstacles in the modeling of multi-agent systems. The developed model is intended for the control program of multi-agent systems, takes into account the stochastic behavior of the environment when used in multi-agent systems. The key differences between the classical stationary random environment and multi-agent systems are presented, including the rationality of agent behavior and the need for cooperation or counteraction. The model does not provide trainable algorithms, their application requires a large sample for training, not all multi-agent systems have enough time to set the necessary statistics and behavior adaptation. Approaches to building the architecture of the agent control program in a multi-agent environment based on the theories of time and probabilistic automata are described, a format for storing models in file storage is proposed. The proposed architecture takes into account the possibility of changing the state of time automata based on the state of probabilistic automata and vice versa. The generalized algorithm of the control program functioning is described. The proposed algorithm makes it possible to implement control programs for multi-agent systems in the case of a finite number of agent states and uses flexible control mechanisms taken from the time machine model. The main results and proposals for further research, including the construction of hierarchical models are presented.