Exploiting submodular value functions for scaling up active perception

In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. For example, a mobile robot takes sensory actions to efficiently navigate in a new environment. While partially observable Markov decision processes (POMDPs) provide a natural model for such problems, reward...

Approximate Solutions for Factored Dec-POMDPs with Many Agents (abstract)

Dec-POMDPs are a powerful framework for planning in multiagent systems, but are provably intractable to solve. This paper proposes a factored forward-sweep policy computation method that tackles the stages of the problem one by one, exploiting weakly coupled structure at each of these stages. An empirical evaluation shows that the loss in...

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...