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

Abstract

Artificial intelligence methods and technologies are increasingly included in human's everyday life. Managing actors in the context of their activities, from the planning stage to the decision-making stage, are faced with the need to operate with big data, non-linear, exponentially growing, critically overloaded data scenarios. In these conditions, the need to introduce artificial intelligence technologies is due to the exhaustion of the intellectual and analytical capabilities of a person. The article discusses a variety of methods and approaches of artificial intelligence, examines the content of key algorithms, models and theories, their strengths and weaknesses in such important areas of the economy as planning and decision-making. The focus is on their classification. Due to the dependence of the planning process on environmental factors, both classical and non-classical planning environments are discussed. If the environment is fully observable, deterministic and static (external changes are ignored) and discrete in terms of time and action, then we are dealing with a classical planning environment. In the case of a partially observable or stochastic environment, we get a non-classical planning environment. The simplest and most intuitive approach to the planning process algorithms is a Total Order Planning. A scheduling algorithm with parallel execution of actions or without specifying the sequence of their execution is a Partial Order Planning algorithm. Recent research into the development of efficient algorithms has sparked interest in one of the earliest planning approaches – Prepositional Logic Planning. With the Critical Path Method, a schedule of activities is drawn up as part of a plan with zero critical travel time margin for each activity, taking into account the calculation of the time margin for each activity and sequence of activities. A forward-looking planning method for solving complex problems is a hierarchical decomposition based on a Hierarchical Task Networks. The influence of time and resource factors on planning procedures is separately highlighted. Approaches and methods used in a non-classical planning environment: compatible planning, conditional planning, continuous planning, multi-agent planning. 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.