Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Constraint Processing
Graphical Models in Local, Asymmetric Multi-Agent Markov Decision Processes
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Agent interaction in distributed POMDPs and its implications on complexity
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Exact solutions of interactive POMDPs using behavioral equivalence
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Exploiting locality of interaction in factored Dec-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Constraint-based dynamic programming for decentralized POMDPs with structured interactions
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
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Agent influence as a predictor of difficulty for decentralized problem-solving
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
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
Offline Planning for Communication by Exploiting Structured Interactions in Decentralized MDPs
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Planning for weakly-coupled partially observable stochastic games
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Abstracting influences for efficient multiagent coordination under uncertainty
Abstracting influences for efficient multiagent coordination under uncertainty
Producing efficient error-bounded solutions for transition independent decentralized mdps
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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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Researchers in the field of multiagent sequential decision making have commonly used the terms "weakly-coupled" and "loosely-coupled" to qualitatively classify problems involving agents whose interactions are limited, and to identify various structural restrictions that yield computational advantages to decomposing agents' centralized planning and reasoning into largely-decentralized planning and reasoning. Together, these restrictions make up a heterogeneous collection of facets of "weakly-coupled" structure that are conceptually related, but whose purported computational benefits are hard to compare evenhandedly. The contribution of this paper is a unified characterization of weak coupling that brings together three complementary aspects of agent interaction structure. By considering these aspects in combination, we derive new bounds on the computational complexity of optimal Dec-POMDP planning, that together quantify the relative benefits of exploiting different forms of interaction structure. Further, we demonstrate how our characterizations can be used to explain why existing classes of decoupled solution algorithms perform well on some problems but poorly on others, as well as to predict the performance of a particular algorithm from identifiable problem attributes.