Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs

This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm, which reduces the problem to a tree of one-shot...

Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion (extended abstract)

We advance the state of the art in optimal solving of decentralized partially observable Markov decision processes (Dec-POMDPs), which provide a formal model for multiagent planning under uncertainty.