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

arXiv: https://arxiv.org/abs/1803.05464 · HTML: https://mlsanity.com/paper/1803.05464/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives](https://mlsanity.com/paper/2304.05147/) (2023)
- [Optimizing robotic swarm based construction tasks](https://mlsanity.com/paper/2106.09749/) (2021)
- [Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning](https://mlsanity.com/paper/2604.06598/) (2026)

## Abstract (arXiv)

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.