Multi-Agent Temporal Task Solving and Plan Optimization
Keywords: Task Decomposition, Plan Optimization, Cooperation, Coordination, Weighted Metrics
TL;DR: We present MA-LAMA, a centralized, unthreaded, satisfying, total-order, multi-agent temporal planner with task decomposition and required cooperation techniques
Abstract: 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 centralized, unthreated, satisfying, total-order, multi-agent temporal planner, that exploits the 'multi-agent nature' of temporal domains to, as its predecessor LAMA, 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 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.
Experimentation shows 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.
Primary Keywords: Temporal Planning, Multi-Agent Planning
Category: Long
Student: Graduate
Submission Number: 291
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