EVOLUTIONARY DESIGN AS A TOOL FOR DEVELOPING MULTI-AGENT SYSTEMS
Abstract
The article proposes a methodology for the evolutionary design of autonomous agents and
multi-agent systems (MAS), on the basis of which the development and implementation of fuzzy
and hybrid models of the formation of agents is carried out. Existing approaches to the design of
information systems based on multi-agent organizations are considered. Analyzed the features and
disadvantages of existing approaches. It is noted that the use of the principles of the theory of evolutionary
development, the development of new approaches that use natural analogues, makes it possible
to increase the effectiveness of the existing methodology for designing multi-agent systems.
A model of agent interaction in a multi-agent system is described. Different approaches to the evolutionary
design of agents and multi-agent systems, which can be based on different models of evolution,
are considered. A formal formulation of the evolutionary design problem of artificial systems is
presented. The fundamental problems that arise when creating a general theory of the evolution of
agents and multi-agent systems are highlighted. The features of various models and levels of evolution
are considered. The concept of designing agents and multi-agent systems has been developed,
according to which the design process includes the basic components of self-organization, including
the processes of interaction, crossing, adaptation to the environment, etc. A model of forming an
agent - a descendant, based on the analysis of possible types of agent interaction - parents in the
process of evolutionary design is proposed. A general methodology for the evolutionary design of
agents and a multi-agent system has been built. Various types of crossover operators were developed
and described, the idea of creating agencies (families) as units of evolving multi-agent systems
was formulated. A software system has been developed and implemented to support evolutionary
design of agents and multi-agent systems. A brief description of the performed computational
experiments confirming the effectiveness of the proposed method is presented.
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