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• W1769870091 abstract "Multiagent planning has seen much progress with the development of formal models such as Dec-POMDPs. However, the complexity of these models--NEXP-Complete even for two agents-- has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical model in which likelihood maximization is equivalent to plan optimization. Using the Expectation-Maximization framework for likelihood maximization, we show that the necessary inference can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We derive a global update rule that combines these local inferences to monotonically increase the overall solution quality. Experiments on a large multiagent planning benchmark confirm the benefits of the new approach in terms of runtime and scalability." @default.
• W1769870091 created "2016-06-24" @default.
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• W1769870091 date "2011-07-16" @default.
• W1769870091 modified "2023-09-23" @default.
• W1769870091 title "Scalable multiagent planning using probabilistic inference" @default.
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• W1769870091 doi "https://doi.org/10.5591/978-1-57735-516-8/ijcai11-357" @default.
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