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Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Decentralized Markov Decision Processes with Event-Driven Interactions
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Approximating state estimation in multiagent settings using particle filters
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Games with Incomplete Information Played by "Bayesian" Players, I-III
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Dynamic programming for partially observable stochastic games
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A particle filtering based approach to approximating interactive POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A framework for sequential planning in multi-agent settings
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Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Graphical models for online solutions to interactive POMDPs
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Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
Improved approximation of interactive dynamic influence diagrams using discriminative model updates
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Speeding up exact solutions of interactive dynamic influence diagrams using action equivalence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Approximating behavioral equivalence of models using top-k policy paths
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Improved use of partial policies for identifying behavioral equivalence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Generalized and bounded policy iteration for finitely-nested interactive POMDPs: scaling up
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Exploiting model equivalences for solving interactive dynamic influence diagrams
Journal of Artificial Intelligence Research
Learning Communication in Interactive Dynamic Influence Diagrams
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a method for transforming the infinite interactive state space of interactive POMDPs (I-POMDPs) into a finite one, thereby enabling the computation of exact solutions. I-POMDPs allow sequential decision making in multi-agent environments by modeling other agents' beliefs, capabilities, and preferences as part of the interactive state space. Since beliefs are allowed to be arbitrarily nested and are continuous, it is not possible to compute optimal solutions using value iteration as in POMDPs. We present a method that transforms the original state space into a finite one by grouping the other agents' behaviorally equivalent models into equivalence classes. This enables us to compute the complete optimal solution for the I-POMDP, which may be represented as a policy graph. We illustrate our method using the multi-agent Tiger problem and discuss features of the solution.