Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy
It provides a structural taxonomy for researchers in multi-agent systems, but is incremental as a review paper.
This paper reviews multi-agent collective behavior algorithms and classifies them by mathematical structure, analyzing their scalability, bandwidth use, and maturity for various coordination tasks.
In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development.