Planning and acting in partially observable stochastic domains
Artificial Intelligence
A language for modeling agents' decision making processes in games
AAMAS '03 Proceedings of the second 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
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
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
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
Improved use of partial policies for identifying behavioral equivalence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
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Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in partially observable settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Previous approach for exactly solving I-DIDs groups together models having similar solutions into behaviorally equivalent classes and updates these classes. We present a new method that, in addition to aggregating behaviorally equivalent models, further groups models that prescribe identical actions at a single time step. We show how to update these augmented classes and prove that our method is exact. The new approach enables us to bound the aggregated model space by the cardinality of other agents' actions. We evaluate its performance and provide empirical results in support.