A study in a hybrid centralised-swarm agent community

Author

Van Aardt, Bradley ; Marwala, Tshilidzi

Author_Institution

Sch. of Electr. & Inf. Eng., Witwatersrand Univ., Johannesburg, South Africa

fYear

2005

fDate

13-16 April 2005

Firstpage

169

Lastpage

174

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 themselves. The captor agents learn through the use of MLP 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.

Keywords

genetic algorithms; learning (artificial intelligence); multi-agent systems; multilayer perceptrons; MLP neural network; captor agent; genetic algorithm; global agent; hybrid centralised-swarm agent; local goal assignment; multiagent system; pursuit game; Africa; Collaboration; Delay; Genetic algorithms; Large-scale systems; Machine learning; Multiagent systems; Neural networks; Pattern recognition; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Cybernetics, 2005. ICCC 2005. IEEE 3rd International Conference on

Print_ISBN

0-7803-9122-5

Type

conf

DOI

10.1109/ICCCYB.2005.1511568

Filename

1511568