The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
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
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
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 Markov Decision Processes (Dec-POMDPs) are powerful theoretical models for deriving optimal coordination policies of agent teams in environments with uncertainty. Unfortunately, their general NEXP solution complexity [3] presents significant challenges when applying them to real-world problems, particularly those involving teams of more than two agents. Inevitably, the policy space becomes intractably large as agents coordinate joint decisions that are based on dissimilar beliefs about an uncertain world state and that involve performing actions with stochastic effects. Our work directly confronts the policy space explosion with the intuition that instead of coordinating all policy decisions, agents need only coordinate abstractions of their policies that constitute the essential influences that they exert on each other.