A factored approach to deterministic contingent multi-agent planning

Shashank Shekhar, Ronen I. Brafman, Guy Shani

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

1 Scopus citations

Abstract

Collaborative Multi-Agent Planning (MAP) under uncertainty with partial observability is a notoriously difficult problem. Such MAP problems are often modeled as Dec- POMDPs, or its qualitative variant, QDec-POMDP, which is essentially a MAP version of contingent planning. The QDec- POMDP model was introduced with the hope that its simpler, non-probabilistic structure will allow for better scalability. Indeed, at least with deterministic actions, the recent IMAP algorithm scales much better than comparable Dec- POMDP algorithms (Bazinin and Shani 2018). In this work we suggest a new approach to solving Deterministic QDec- POMDPs based on problem factoring. First, we find a solution to a MAP problem where the results of any observation is available to all agents. This is essentially a single-agent planning problem for the entire team. Then, we project the solution tree into sub-trees, one per agent, and let each agent transform its projected tree into a legal local tree. If all agents succeed, we combine the trees into a valid joint-plan. Otherwise, we continue to explore the space of team solutions. This approach is sound, complete, and as our empirical evaluation demonstrates, scales much better than the IMAP algorithm.

Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Automated Planning and Scheduling, ICAPS 2019
EditorsJ. Benton, Nir Lipovetzky, Eva Onaindia, David E. Smith, Siddharth Srivastava
PublisherAAAI press
Pages419-427
Number of pages9
ISBN (Electronic)9781577358077
StatePublished - 1 Jan 2019
Event29th International Conference on Automated Planning and Scheduling, ICAPS 2019 - Berkeley, United States
Duration: 11 Jul 201915 Jul 2019

Publication series

NameProceedings International Conference on Automated Planning and Scheduling, ICAPS
ISSN (Print)2334-0835
ISSN (Electronic)2334-0843

Conference

Conference29th International Conference on Automated Planning and Scheduling, ICAPS 2019
Country/TerritoryUnited States
CityBerkeley
Period11/07/1915/07/19

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