Modeling and solving the multi-agent pathfinding problem in picat

Roman Bartak, Neng Fa Zhou, Roni Stern, Eli Boyarski, Pavel Surynek

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

17 Scopus citations

Abstract

The multi-Agent pathfinding (MAPF) problem has attracted considerable attention because of its relation to practical applications. In this paper, we present a constraint-based declarative model for MAPF, together with its implementation in Picat, a logic-based programming language. We show experimentally that our Picat-based implementation is highly competitive and sometimes outperforms previous approaches. Importantly, the proposed Picat implementation is very versatile. We demonstrate this by showing how it can be easily adapted to optimize different MAPF objectives, such as minimizing makespan or minimizing the sum of costs, and for a range of MAPF variants. Moreover, a Picat-based model can be automatically compiled to several general-purpose solvers such as SAT solvers and Mixed Integer Programming solvers (MIP). This is particularly important for MAPF because some MAPF variants are solved more efficiently when compiled to SAT while other variants are solved more efficiently when compiled to MIP. We analyze these differences and the impact of different declarative models and encodings on empirical performance.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Tools with Artificial Intelligence, ICTAI 2017
PublisherIEEE Computer Society
Pages959-966
Number of pages8
ISBN (Electronic)9781538638767
DOIs
StatePublished - 4 Jun 2018
Event29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017 - Boston, United States
Duration: 6 Nov 20178 Nov 2017

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2017-November
ISSN (Print)1082-3409

Conference

Conference29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017
Country/TerritoryUnited States
CityBoston
Period6/11/178/11/17

Keywords

  • Modeling
  • Multi-Agent
  • SAT
  • pathfinding

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