FMAP: Distributed Cooperative Multi-Agent Planning

TL;DR

FMAP is a distributed multi-agent planning method that efficiently solves complex cooperative tasks by integrating planning, coordination, and privacy-preserving communication, outperforming existing systems on benchmark problems.

Contribution

Introduces FMAP, a novel fully-distributed multi-agent planning approach with a new heuristic and privacy model, capable of handling diverse cooperative planning problems.

Findings

FMAP outperforms current MAP systems on benchmark tasks.

The $h_{DTG}$ heuristic effectively guides distributed plan search.

FMAP efficiently solves both tightly- and loosely-coupled domains.

Abstract

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by $h_{DTG}$, a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.