Task Allocation in Multi-Agent Systems with Grammar-Based Evolution

This paper presents a grammar-based evolutionary model to facilitate autonomous emergence of task allocation for intelligent multi-agent systems. The approach adopts a context-free grammar to determine the behaviour rule syntax. This allows for flexibility in evolving task allocation under multiple and dynamic constraints without manual rule design and parameter tuning. Experimental evaluations conducted with a target discovery simulation illustrate that the grammar-based model performs successfully in both dynamic and non-dynamic conditions. A statistically significant performance improvement is shown compared to an algorithm developed with the broadcast of local eligibility mechanism and a genetic programming mechanism. Grammatical evolution can achieve near-optimal solutions under restrictions applied on the number of agents, targets and the time allowed. Further, analysis of the evolved rule structures shows that grammatical evolution can identify less complex rule structures for behaviours while maintaining the expected level of performance. The results infer that the proposed model is a promising alternative for dynamic task allocation with human interactions in complex real-world domains.