General branch and bound, and its relation to A* and AO*
Artificial Intelligence
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
The complexity of multiagent systems: the price of silence
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Dynamic Programming
Letting loose a SPIDER on a network of POMDPs: generating quality guaranteed policies
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Value-based observation compression for DEC-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Point-based dynamic programming for DEC-POMDPs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Heuristic search for identical payoff Bayesian games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Point-based policy generation for decentralized POMDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Point-based backup for decentralized POMDPs: complexity and new algorithms
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Online planning for multi-agent systems with bounded communication
Artificial Intelligence
Toward error-bounded algorithms for infinite-horizon DEC-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Social Model Shaping for Solving Generic DEC-POMDPs
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Solving decentralized POMDP problems using genetic algorithms
Autonomous Agents and Multi-Agent Systems
Producing efficient error-bounded solutions for transition independent decentralized mdps
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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Recent scaling up of decentralized partially observable Markov decision process (DEC-POMDP) solvers towards realistic applications is mainly due to approximate methods. Of this family, Memory Bounded Dynamic Programming (MBDP), which combines in a suitable manner top-down heuristics and bottom-up value function updates, can solve DEC-POMDPs with large horizons. The performances of MBDP, can be, however, drastically improved by avoiding the systematic generation and evaluation of all possible policies which result from the exhaustive backup. To achieve that, we suggest a heuristic search method, namely Point Based Incremental Pruning (PBIP), which is able to distinguish policies with different heuristic estimates. Taking this insight into account, PBIP searches only among the most promising policies, finds those useful, and prunes dominated ones. Doing so permits us to reduce clearly the amount of computation required by the exhaustive backup. The computation experiment shows that PBIP solves DEC-POMDP benchmarks up to 800 times faster than the current best approximate algorithms, while providing solutions with higher values.