# Cooperative Multi-Agent Planning: A Survey

arXiv: https://arxiv.org/abs/1711.09057 · HTML: https://mlsanity.com/paper/1711.09057/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner](https://mlsanity.com/paper/2409.20560/) (2024)
- [Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees](https://mlsanity.com/paper/2501.16539/) (2025)
- [Resonating Minds -- Emergent Collaboration Through Hierarchical Active Inference](https://mlsanity.com/paper/2112.01210/) (2021)
- [SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT](https://mlsanity.com/paper/2105.14915/) (2021)
- [Argument Schemes and Dialogue for Explainable Planning](https://mlsanity.com/paper/2101.02648/) (2021)

## Abstract (arXiv)

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This paper reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.