Distributed snapshots: determining global states of distributed systems
ACM Transactions on Computer Systems (TOCS)
Best-first heuristic search for multi-core machines
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
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Search is among the most fundamental techniques for problem solving, and A* is probably the best known heuristic search algorithm. In this paper we adapt A* to the multiagent setting, focusing on multi-agent planning problems. We provide a simple formulation of multi-agent A*, with a parallel and distributed variant. Our algorithms exploit the structure of multi-agent problems to not only distribute the work efficiently among different agents, but also to remove symmetries and reduce the overall workload. Given a multi-agent planning problem in which agents are not tightly coupled, our parallel version of A* leads to super-linear speedup, solving benchmark problems that have not been solved before. In its distributed version, the algorithm ensures that private information is not shared among agents, yet computation is still efficient -- sometimes even more than centralized search -- despite the fact that each agent has access to partial information only.