EgoPlan : A framework for multi-agent planning using single agent planners
McArthur, Mark and Moshfeghi, Yashar and Cashmore, Michael (2022) EgoPlan : A framework for multi-agent planning using single agent planners. Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS, 35. 130647. ISSN 2334-0762 (https://doi.org/10.32473/flairs.v35i.130647)
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Abstract
Planning problems are, in general, PSPACE-complete; large problems, especially multi-agent problems with required coordination, can be intractable or impractical to solve. Factored planning and multi-agent planning both address this by separating multi-agent problems into tractable sub-problems, but there are limitations in the expressivity of existing planners and in the ability to handle tightly coupled multi-agent problems. This paper presents EGOPLAN, a framework which factors a multi-agent problem into related sub-problems which are solved by iteratively calling on a single agent planner. EGOPLAN is evaluated on a multi-robot test domain with durative actions, required coordination, and temporal constraints, comparing the performance of a temporal planner, OPTIC-CPLEX, with and without EGOPLAN. Our results show that for our test domain, using EGOPLAN allows OPTIC-CPLEX to solve problems that are twice as complex as it can solve without EGOPLAN, and to solve complex problems significantly faster.
ORCID iDs
McArthur, Mark, Moshfeghi, Yashar and Cashmore, Michael
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Item type: Article ID code: 81372 Dates: DateEvent4 May 2022Published28 March 2022AcceptedKeywords: factored planning, multi-agent planning, multi-robot planning, system architectures, task planning, Electronic computers. Computer science, Artificial Intelligence, Software Subjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 06 Jul 2022 14:11 Last modified: 25 Nov 2023 02:42 URI: https://strathprints.strath.ac.uk/id/eprint/81372