Behaviour discovery in real-time strategy games using cooperative co-evolution with dynamic binary tree decomposition
Abstract
In cooperative co-evolutionary approaches, a larger global problem is typically decomposed into multiple sub-components, with solutions concurrently evolved for each. Problem decomposition commonly occurs prior to the execution of the algorithm, but this assumes that the optimal decomposition is already known. However, the optimal decomposition is typically unknown for problems with a large number of components, such as multi-agent systems featuring many agents, where the challenge of decomposing the problem grows with the scale of the number of agents involved. In this paper, we propose a dynamic decomposition scheme based on determining optimal groupings of agents, whilst simultaneously evolving the agent behaviour, specifically addressing scalability challenges inherent in multi-agent systems featuring a large number of agents. The approach was evaluated in a real-time strategy game by evolving the behaviour of a heterogeneous team of agents representing combat units. Four scenarios were investigated featuring an increasingly larger number of agents. We compared our approach against two cooperative co-evolutionary algorithms involving commonly used static decomposition schemes. The proposed dynamic decomposition approach matched or exceeded the performance of these two static schemes across all scenarios.
Document Type
Conference Proceeding
Date of Publication
7-14-2024
Publication Title
GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
Publisher
Association for Computing Machinery
School
School of Engineering
RAS ID
71869
Copyright
subscription content
First Page
287
Last Page
290
Comments
Kelly, L., Masek, M., & Lam, C. P. (2024, July). Behaviour discovery in real-time strategy games using cooperative co-evolution with dynamic binary tree decomposition. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 287-290). https://doi.org/10.1145/3638530.3654222