Heuristic search of multiagent influence space

Multiagent planning under uncertainty has seen important progress in recent years. Two techniques, in particular, have substantially advanced efficiency and scalability of planning. Multiagent heuristic search gains traction by pruning large portions of the joint policy space deemed suboptimal by heuristic bounds. Alternatively, influence-based abstraction reformulates the search space of joint policies into a smaller space of influences, which represent the probabilistic effects that agents' policies may exert on one another. These techniques have been used independently, but never together, to solve larger problems (for Dec-POMDPs and subclasses) than previously possible. In this paper, we take the logical albeit nontrivial next step of combining multiagent A* search and influence-based abstraction into a single algorithm. The mathematical foundation that we provide, such as partially-specified influence evaluation and admissible heuristic definition, enables an investigation into whether the two techniques bring complementary gains. Our empirical results indicate that A* can provide significant computational savings on top of those already afforded by influence-space search, thereby bringing a significant contribution to the field of multiagent planning under uncertainty.