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
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
Approximate solutions of interactive dynamic influence diagrams using model clustering
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
A framework for sequential planning in multi-agent settings
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - 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
Epsilon-Subjective Equivalence of Models for Interactive Dynamic Influence Diagrams
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Approximating behavioral equivalence of models using top-k policy paths
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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
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Interactive multiagent decision making often requires to predict actions of other agents by solving their behavioral models from the perspective of the modeling agent. Unfortunately, the general space of models in the absence of constraining assumptions tends to be very large thereby making multiagent decision making intractable. One approach that can reduce the model space is to cluster behaviorally equivalent models that exhibit identical policies over the whole planning horizon. Currently, the state of the art on identifying equivalence of behavioral models compares partial policy trees instead of entire trees. In this paper, we further improve the use of partial trees for the identification purpose and develop an incremental comparison strategy in order to efficiently ascertain the model equivalence. We investigate the improved approach in a well-defined probabilistic graphical model for sequential multiagent decision making - interactive dynamic influence diagrams, and evaluate its performance over multiple problem domains.