Multi-Agent Temporal Task Solving and Plan Optimization
conference paper
Several multi-agent techniques are utilized to reduce the complexity of classical planning tasks, however, their applicability to temporal planning domains is a currently open line of study in the field of Automated Planning. In this paper, we present MA-LAMA, a factored, centralized, unthreated, satisfying, multi-agent temporal planner, that exploits the'multi-agent nature' of temporal domains to perform plan optimization. In MA-LAMA, temporal tasks are translated to the constrained snap-actions paradigm, and an automatic agent decomposition, goal assignment, and required cooperation analysis are carried out to build independent search steps, called Search Phases. These Search Phases are then solved by consecutive agent local searches, using classical heuristics and temporal constraints. Experiments show that MA-LAMA is able to solve a wide range of classical and temporal multi-agent domains, performing significantly better in plan quality than other state-of-the-art temporal planners. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Topics
TNO Identifier
996794
ISSN
23340835
Publisher
Association for the Advancement of Artificial Intelligence
Source title
Proceedings International Conference on Automated Planning and Scheduling, ICAPS
Collation
8 p.
Files
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