A multi-agent system of artificial intelligence forming principles.
Abstract
The article discloses the principles of forming a multi-agent system of artificial intelligence. Within the framework of the study, the concepts of an agent, a multi-agent system are considered, the main types of agent architectures and a generalized approach to the construction of distributed intelligent information systems using multi-agent technologies are considered. It is emphasized that the problems of artificial agents and multi-agent systems are based on achievements obtained in the framework of works on distributed artificial intelligence, distributed problem solving and parallel artificial intelligence. It is emphasized that the basic concept underlying multi-agent technologies is the concept of an agent. An agent is defined as an entity that can perceive the environment using receptors and interact with it, that is, an agent is an entity capable of perceiving the environment in the form of sensors and influencing the environment in the form of executive mechanisms. It is noted that traditionally the architectures of artificial agents are divided into three groups: reactive, deliberative and hybrid agent architectures. It is substantiated that the ontology of a subject field usually characterizes the intellectual properties of agents, that is, the more accurately and correctly the ontology is built with marked connections between concepts, the more fully the agent represents the subject area for which it exists. The method of designing multi-agent systems is outlined, which is based on the principle of distribution of functions between all agents of the system. Such a system, in its essence, is a set of separate intelligent systems, each of which solves its task according to the principle of distributed problem solving. It is emphasized that when solving complex tasks using a multi-agent approach, there is often a need to divide it into subtasks that are assigned to individual agents. It is emphasized that many agents of the system can interpret tasks from different points of view and then integrate the obtained results. It is noted that the functional distribution of application programs makes it possible to eliminate many shortcomings of classic expert systems.
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