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JOURNALS // Informatika i Ee Primeneniya [Informatics and its Applications] // Archive

Inform. Primen., 2021 Volume 15, Issue 3, Pages 29–40 (Mi ia741)

This article is cited in 1 paper

Expert system for monitoring and forecasting of resource allocation processes

A. V. Bosov, D. V. Zhukov

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The paper presents a project of an expert monitoring system designed to support decision-making in managing the processes of distribution (consumption and reproduction) of resources. The results of the analysis of the consumption process are presented in the form of scenarios for the integral assessment of its state. Scenarios are prepared by an expert, are situational in nature and are used in real calculations both to assess the current state and to predict the development of situations. The traditional interpretation of the consumption process is based on the concept of resources and subjects of consumption who consume resources in accordance with consumption rates, as well as a simple description of the reproduction of resources by production objects. It is assumed that the components of the information model are geographically and temporally referenced, in particular, data on resource reserves are linked to time. The main intellectual load is carried by the methodology for preparing scenarios for the integral assessment of the state. This technique is based on the ideology of expert evaluation of typical situations and the formation of calculation scenarios using simple machine learning methods. The latter use widespread approaches to optimization — minimization of mean squares and modules. The presented project of the expert system is instrumental in nature and can be used in various applications. The process of preparing and evaluating the effectiveness of the scenarios of integral assessment prepared by the expert is illustrated by numerical and graphic material.

Keywords: expert system, resources and consumption, machine learning, least squares method, least modules method.

Received: 04.06.2021

DOI: 10.14357/19922264210305



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