An approach to multi-agent planning with incomplete information

TL;DR

This paper introduces a versatile multi-agent planning method that efficiently manages tightly-coupled tasks, allowing agents to operate with incomplete information and private data, outperforming traditional distributed CSP-based approaches.

Contribution

It presents a novel cooperative refinement planning approach based on partial-order planning that handles any level of agent coupling with incomplete information.

Findings

Outperforms distributed CSP-based MAP in experiments

Handles incomplete information and private data effectively

Applicable to tightly-coupled multi-agent problems

Abstract

Multi-agent planning (MAP) approaches have been typically conceived for independent or loosely-coupled problems to enhance the benefits of distributed planning between autonomous agents as solving this type of problems require less coordination between the agents' sub-plans. However, when it comes to tightly-coupled agents' tasks, MAP has been relegated in favour of centralized approaches and little work has been done in this direction. In this paper, we present a general-purpose MAP capable to efficiently handle planning problems with any level of coupling between agents. We propose a cooperative refinement planning approach, built upon the partial-order planning paradigm, that allows agents to work with incomplete information and to have incomplete views of the world, i.e. being ignorant of other agents' information, as well as maintaining their own private information. We show various experiments to compare the performance of our system with a distributed CSP-based MAP approach over a suite of problems.