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
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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
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Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of behavioral models ascribed to other agents over time. Previous approaches mainly cluster behaviorally equivalent models to reduce the complexity of I-DID solutions. In this paper, we seek to further reduce the model space by introducing an approximate measure of behavioral equivalence (BE) and using it to group models. Specifically, we focus on $K$ most probable paths in the solution of each model and compare these policy paths to determine approximate BE. We discuss the challenges in computing the top $K$ policy paths and experimentally evaluate the performance of this heuristic approach in terms of the scalability and quality of the solution.