Computationally feasible bounds for partially observed Markov decision processes
Operations Research
A complementarity approach to a quasistatic multi-rigid-body contact problem
Computational Optimization and Applications
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
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
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Average-reward decentralized Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bounded-Resource Reasoning as (Strong or Classical) Planning
Computational Logic in Multi-Agent Systems
Interaction structure and dimensionality reduction in decentralized MDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Communication-based decomposition mechanisms for decentralized MDPs
Journal of Artificial Intelligence Research
A bilinear programming approach for multiagent planning
Journal of Artificial Intelligence Research
Efficient and distributable methods for solving the multiagent plan coordination problem
Multiagent and Grid Systems - Planning in multiagent systems
Planning in stochastic domains for multiple agents with individual continuous resource state-spaces
Autonomous Agents and Multi-Agent Systems
Robust Approximate Bilinear Programming for Value Function Approximation
The Journal of Machine Learning Research
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Developing scalable coordination algorithms for multi-agent systems is a hard computational challenge. One useful approach, demonstrated by the Coverage Set Algorithm (CSA), exploits structured interaction to produce significant computational gains. Empirically, CSA exhibits very good anytime performance, but an error bound on the results has not been established. We reformulate the algorithm and derive both online and offline error bounds for approximate solutions. Moreover, we propose an effective way to automatically reduce the complexity of the interaction. Our experiments show that this is a promising approach to solve a broad class of decentralized decision problems. The general formulation used by the algorithm makes it both easy to implement and widely applicable to a variety of other AI problems.