Task Modelling in Collective Robotics
Autonomous Robots
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Scaling up: solving POMDPs through value based clustering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
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
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solving POMDPs with continuous or large discrete observation spaces
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Constraint-based dynamic programming for decentralized POMDPs with structured interactions
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Lossless clustering of histories in decentralized POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Reward shaping for valuing communications during multi-agent coordination
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Agent Influence and Intelligent Approximation in Multiagent Problems
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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Representing agent policies compactly is essential for improving the scalability of multi-agent planning algorithms. In this paper, we focus on developing a pruning technique that allows us to merge certain observations within agent policies, while minimizing loss of value. This is particularly important for solving finite-horizon decentralized POMDPs, where agent policies are represented as trees, and where the size of policy trees grows exponentially with the number of observations. We introduce a value-based observation compression technique that prunes the least valuable observations while maintaining an error bound on the value lost as a result of pruning. We analyze the characteristics of this pruning strategy and show empirically that it is effective. Thus, we use compact policies to obtain signicantly higher values compared with the best existing DEC-POMDP algorithm.