Approach to knowledge management and the development of a multi-agent knowledge representation and processing system

Objectives. A multi-agent knowledge representation and processing system (MKRPS) comprises a distributed artificial intelligence system designed to solve problems that are difficult or impossible to solve using monolithic systems. Solving complex problems in an MKRPS is accomplished by communities of intelligent software agents that use cognitive data structures, logical inference, and machine learning. Intelligent software agents are able to act rationally under conditions of incompleteness and ambiguity of incoming information. The aim of the present work is to identify models and methods, as well as software modules and tools, for use in developing a highly efficient MKRPS.Methods. Agent-based modeling methods were used to formally describe and programmatically simulate the rational behavior of intelligent agents, expert evaluation methods, the mathematical apparatus of automata theory, Markov chains, fuzzy logic, neural networks, and reinforcement learning.Results. An MKRPS structure diagram, a multi-agent solver, and microservices access control diagram were developed. Methods for distribution of intelligent software agents on the MKRPS nodes are proposed along with algorithms for optimizing the logical structure of the distributed knowledge base (DKB) to improve the performance of the MKRPS in terms of volume, cost and time criteria.Conclusions. The proposed approach to the development and use of intelligent software agents combines knowledge-based reasoning mechanisms with neural network models. The developed MKRPS structure and DKB control diagram includes described methods for optimizing the DKB, determining the availability of microservices used by the agents, ensuring the reliability assurance and coordinated functioning of the computing nodes of the system, as well as instrumental software tools to simplify the design and implementation of the MKRPS. The results demonstrate the effectiveness of the presented approach to knowledge management and the development of a high-performance problem-oriented MKRPS.