From one to many: Planning for loosely coupled multi-agent systems

Ronen I. Brafman, Carmel Domshlak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

172 Scopus citations

Abstract

Loosely coupled multi-agent systems are perceived as easier to plan for because they require less coordination between agent sub-plans. In this paper we set out to formalize this intuition. We establish an upper bound on the complexity of multi-agent planning problems that depends exponentially on two parameters quantifying the level of agents' coupling, and on these parameters only. The first parameter is problemindependent, and it measures the inherent level of coupling within the system. The second is problem-specific and it has to do with the minmax number of action-commitments per agent required to solve the problem. Most importantly, the direct dependence on the number of agents, on the overall size of the problem, and on the length of the agents' plans, is only polynomial. This result is obtained using a new algorithmic methodology which we call "planning as CSP+planning". We believe this to be one of the first formal results to both quantify the notion of agents' coupling, and to demonstrate a multi-agent planning algorithm that, for fixed coupling levels, scales polynomially with the size of the problem.

Original languageEnglish
Title of host publicationICAPS 2008 - Proceedings of the 18th International Conference on Automated Planning and Scheduling
Pages28-35
Number of pages8
StatePublished - 1 Dec 2008
Event18th International Conference on Automated Planning and Scheduling, ICAPS 2008 - Sydney, NSW, Australia
Duration: 14 Sep 200818 Sep 2008

Publication series

NameICAPS 2008 - Proceedings of the 18th International Conference on Automated Planning and Scheduling

Conference

Conference18th International Conference on Automated Planning and Scheduling, ICAPS 2008
Country/TerritoryAustralia
CitySydney, NSW
Period14/09/0818/09/08

ASJC Scopus subject areas

  • Strategy and Management
  • Artificial Intelligence
  • Computer Science Applications

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