Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy

TL;DR

This paper provides a comprehensive review and classification of multi-agent collective behavior algorithms based on their mathematical structures, analyzing their applications, scalability, and maturity to guide future research.

Contribution

It introduces a structural taxonomy of multi-agent algorithms, linking mathematical techniques to coordination tasks and highlighting research gaps and adoption challenges.

Findings

Artificial potential functions are versatile for various coordination tasks.

Many complex algorithms face slow adoption in practical applications.

The paper identifies key areas for future research and development.

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.