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Planning under uncertainty in multiagent settings is highly intractable because of history and plan space complexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the computational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the algorithm for exactly solving interactive dynamic influence diagrams, which is a recognized graphical models for multiagent planning. Beyond parallelizing the standard Bayesian inference, the computation of decisions' expected utilities are parallelized. The GPU-based approach provides significant speedup on two benchmark problems.

Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures