A method for synthesizing and automatically adapting the architecture and organizational structure of a multi-agent system
Abstract
A method for synthesizing and automatically adapting the architecture and organizational structure of a multi-agent system
Incoming article date: 24.12.2025This paper examines the pressing issue of designing multi-agent systems (MAS) that require high adaptability in the face of dynamically changing environmental parameters, task conditions, and the internal structure of the MAS. A comprehensive method is proposed that combines the processes of synthesizing the initial architecture and subsequently automatically adapting both the MAS architecture, which defines the basic rules of its operation, and the organizational structure, which represents a hierarchical attributed directed graph in which the vertices are agents, and the edges correspond to the relationships between them. The method is based on the author's "zoosocial model" for representing the MAS architecture. It also utilizes an artificial neural network to predict optimal actions for modifying the MAS architecture in the event of changes in environmental parameters and task conditions. The REINFORCE algorithm is used for training the ANS, and a genetic algorithm is used to generate the training sample.
Keywords: multi-agent system, architecture, organizational structure, automatic adaptation, synthesis, genetic algorithm, reinforcement learning