REVIEW OF THEORETICAL APPROACHES TO USING OF ARTIFICIAL INTELLIGENCE FOR PLANNING PROBLEMS IN ECONOMICS

Special attention is paid to the issues of constructing planning models in conditions of uncertainty based on the theoretical-probabilistic (stochastic) approaches. Bayesian networks are used to represent vagueness. The Relational Probability Model includes certain constraints on the presentation means, thereby guaranteeing a fully defined probability distributions. The main tasks of probabilistic representation in temporal models are: filtering, forecasting, smoothing, determining a probabilistic explanation. By combining these algorithms and additional enhancements, three large blocks of temporal models can be obtained: Hidden Markov Models, Kalman Filter, and Dynamic Bayesian Network. Decision theory allows the agent to determine the sequence of actions to be performed. A simpler formal system for solving decision-making problems is decision-making networks. The use of expert systems containing information about utility creates additional opportunities. Sequential multiple decision problems in an uncertain environment, such as Markov Decision Processes, are defined using transition models. When several agents interact simultaneously, game theory is used to describe the rational behavior of agents. As we can see, planning has recently become one of the most interesting and relevant directions in the field of artificial intelligence research. There is still a long way to go: it is necessary to develop a clear vision of the problem of choosing the appropriate specific methods depending on the type of task, perhaps by creating completely new methods and approaches.