Cooperative Multi-Agent Planning: A Survey

TL;DR

This survey reviews recent advances in cooperative multi-agent planning, focusing on solver approaches from the 2015 competition, and categorizes them based on features and performance.

Contribution

It provides a comprehensive classification and comparison of MAP solvers, highlighting developments and challenges in the field.

Findings

Solver approaches vary significantly in performance and features.

The 2015 competition revealed key trends and gaps in MAP algorithms.

Cooperative planning techniques are evolving with new algorithms and benchmarks.

Abstract

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.