Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Computing Factored Value Functions for Policies in Structured MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
Exploiting structure in decentralized markov decision processes
Exploiting structure in decentralized markov decision processes
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
Not all agents are equal: scaling up distributed POMDPs for agent networks
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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
Forward search value iteration for 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
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Event-detecting multi-agent MDPs: complexity and constant-factor approximation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Message-passing algorithms for large structured decentralized POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Solving decentralized POMDP problems using genetic algorithms
Autonomous Agents and Multi-Agent Systems
Approximate solutions for factored Dec-POMDPs with many agents
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
Optimally solving dec-POMDPs as continuous-state MDPs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Automated generation of interaction graphs for value-factored dec-POMDPs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Decentralized partially observable MDPs (DEC-POMDPs) provide a rich framework for modeling decision making by a team of agents. Despite rapid progress in this area, the limited scalability of solution techniques has restricted the applicability of the model. To overcome this computational barrier, research has focused on restricted classes of DEC-POMDPs, which are easier to solve yet rich enough to capture many practical problems. We present CBDP, an efficient and scalable point-based dynamic programming algorithm for one such model called ND-POMDP (Network Distributed POMDP). Specifically, CBDP provides magnitudes of speedup in the policy computation and generates better quality solution for all test instances. It has linear complexity in the number of agents and horizon length. Furthermore, the complexity per horizon for the examined class of problems is exponential only in a small parameter that depends upon the interaction among the agents, achieving significant scalability for large, loosely coupled multi-agent systems. The efficiency of CBDP lies in exploiting the structure of interactions using constraint networks. These results extend significantly the effectiveness of decision-theoretic planning in multi-agent settings.