Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy
Abstract
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.
Cite
@article{arxiv.1803.05464,
title = {Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy},
author = {Federico Rossi and Saptarshi Bandyopadhyay and Michael Wolf and Marco Pavone},
journal= {arXiv preprint arXiv:1803.05464},
year = {2018}
}
Comments
Six pages, one table. To be presented at NAASS 2018