240 citations · 1.1k across the 39 of their papers we have counts for
8 papers · 1 filter
Symbolic Generalization for On-line Planning
Zhengzhu Feng, Eric A. Hansen, Shlomo Zilberstein
Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line pl…
Region-Based Incremental Pruning for POMDPs
Zhengzhu Feng, Shlomo Zilberstein
We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dyna…
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
Daniel Szer, Francois Charpillet, Shlomo Zilberstein
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) wit…
Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs
Sven Seuken, Shlomo Zilberstein
Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in solving decentralized POMDPs with large horizons. We generalize the algorithm and improve its scalabilit…
Optimizing Memory-Bounded Controllers for Decentralized POMDPs
Christopher Amato, Daniel S Bernstein, Shlomo Zilberstein
We present a memory-bounded optimization approach for solving infinite-horizon decentralized POMDPs. Policies for each agent are represented by stochastic finite state controllers.…
Rollout Sampling Policy Iteration for Decentralized POMDPs
Feng Wu, Shlomo Zilberstein, Xiaoping Chen
We present decentralized rollout sampling policy iteration (DecRSPI) - a new algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve sc…