Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third 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
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
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
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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
Exploiting locality of interaction in factored Dec-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Introducing Communication in Dis-POMDPs with Locality of Interaction
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Network Distributed POMDP with Communication
New Frontiers in 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
Planning with continuous resources for agent teams
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Optimal and approximate Q-value functions for decentralized POMDPs
Journal of Artificial Intelligence Research
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Introducing communication in Dis-POMDPs with locality of interaction
Web Intelligence and Agent Systems
Solving efficiently Decentralized MDPs with temporal and resource constraints
Autonomous Agents and Multi-Agent Systems
Heuristic search of multiagent influence space
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
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Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the significant complexity of solving distributed POMDPs, particularly as we scale up the numbers of agents, one popular approach has focused on approximate solutions. Though this approach is efficient, the algorithms within this approach do not provide any guarantees on solution quality. A second less popular approach focuses on global optimality, but typical results are available only for two agents, and also at considerable computational cost. This paper overcomes the limitations of both these approaches by providing SPIDER, a novel combination of three key features for policy generation in distributed POMDPs: (i) it exploits agent interaction structure given a network of agents (i.e. allowing easier scale-up to larger number of agents); (ii) it uses a combination of heuristics to speedup policy search; and (iii) it allows quality guaranteed approximations, allowing a systematic tradeoff of solution quality for time. Experimental results show orders of magnitude improvement in performance when compared with previous global optimal algorithms.