Distributed Adaptation of the Functional Integration Structure of a Multi-agent System in a Dual-tasking Environment

Abstract

The paper considers the problem of distributed adaptation of the functional integration structure of a multi-agent system in a dual-tasking environment from the point of view of organizing multi-agent search and use of the functional emergence effect provided by different structures of functional integration. The considered problem belongs to a wider class of problems of structural adaptation and self-organization. Models of functional integration, in particular, models based on general quantitative characteristics of the functional roles distribution of agents and models based on local qualitative characteristics of the functional roles distribution of agents, taking into account the specifics of functional links established between agents have been considered in the paper. The problems of the distributed adaptation of the functional integration structure have been analyzed, including the problem of the functional specialization of agents in a multitasking environment. Various ways of organizing structural changes have been considered, including multi-agent parametric adaptation based on a local structural parameter. Multi-agent structural adaptation based on reinforcement learning methods, in particular, multi-agent structural adaptation based on the normalized exponential function method (MSA-softmax) and multi-agent structural adaptation based on the upper confidence bound method (MSA-UCB) has been proposed. The distributed adaptation methods simulation results have been presented, which showed the advantage of multi-agent structural adaptation over multi-agent parametric adaptation.