A Study in a Hybrid Centralised-Swarm Agent Community

TL;DR

This paper presents a hybrid multi-agent system combining centralized and swarm approaches, where agents learn via neural networks and a global agent uses genetic algorithms to solve pursuit game problems.

Contribution

It introduces a novel architecture integrating neural networks and genetic algorithms for agent learning and coordination in a hybrid multi-agent system.

Findings

Agents successfully learn to solve board positions.

Global agent effectively guides agent responses.

System demonstrates cooperative problem-solving.

Abstract

This paper describes a systems architecture for a hybrid Centralised/Swarm based multi-agent system. The issue of local goal assignment for agents is investigated through the use of a global agent which teaches the agents responses to given situations. We implement a test problem in the form of a Pursuit game, where the Multi-Agent system is a set of captor agents. The agents learn solutions to certain board positions from the global agent if they are unable to find a solution. The captor agents learn through the use of multi-layer perceptron neural networks. The global agent is able to solve board positions through the use of a Genetic Algorithm. The cooperation between agents and the results of the simulation are discussed here. .