| Abstract: |  Deterministic domain-independent planning techniques for multiagent systems stem from the principles of classical planning. Three most recently studied approaches comprise (i) DisCSP+Planning utilizing distributed Constraint Satisfaction Problem solving for coordination of the agents and individual planning using local search, (ii) multiagent adaptation of A* with local heuristics and (iii)distribution of GraphPlan approach based on merging of planning graphs.
In this work, I summarize the principles of these three approaches and describe a novel implementation and optimizations of the Distributed Planning through Plan Merging (DPGM) multiagent GraphPlan approach. Domain and problem description were adapted for their utilization in multiagent planners. I experimentally validate influence of the parametrization of inner extraction phase of individual plans and compare the best results with the former two multiagent planning techniques.
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