Multi-agent Coordination Algorithms for Pursuit-Evasion

The real-world application of multi-agent systems (MAS) is still challenging and limited, despite the exclusive advantage of MAS over single agent systems. The multi-agent coordination or swarm intelligence is a paramount concern. Based on pursuit-evasion games, this thesis investigates the safe distributed implicit coordination of large-scale multi-agent systems, as some self-organizing systems in nature. Particularly, five research questions are answered in the thesis: (1) how to resolve collisions in the multi-agent environment; (2) how to be cooperative in a distributed way; (3) how to allocate the multi-agent task; (4) how to overcome the partial observation; and (5) how to get the arms race from the adversarial co-evolution. Accordingly, a safety-constrained multi-agent pursuit-evasion platform: MatrixWorld is proposed, and three coordination algorithms are designed, i.e., the cooperative coevolutionary particle swarm optimization (CCPSO-R) algorithm, the biquadratic assignment problem solver and parallel CCPSO-R (BiPCCR) algorithm, and the fuzzy self-organizing cooperative coevolution (FSC2) algorithm. Furthermore, the co-evolution mechanism, autocurricula, self-play, arms races, and adversarial learning are explored in the multi-agent setting by literature review and experimental verification. Safe coordination of thousands of agents is achieved in the experiments.

Author dc:creator

• Sun, Lijun

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• © 2023 Lijun Sun
• au.edu.uts.lib/cph

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10453/177149

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/177149