Approximating behavioral equivalence of models using top-k policy paths
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Approximating Model Equivalence in Interactive Dynamic Influence Diagrams Using Top K Policy Paths
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Improved use of partial policies for identifying behavioral equivalence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Interactive dynamic influence diagrams (I-DID) 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 candidate models ascribed to other agents, over time. Pruning behaviorally equivalent models is one way toward minimizing the model set. We seek to further reduce the complexity by additionally pruning models that are approximately subjectively equivalent. Toward this, we define subjective equivalence in terms of the distribution over the subject agent's future action-observation paths, and introduce the notion of epsilon-subjective equivalence. We present a new approximation technique that reduces the candidate model space by removing models that are epsilon-subjectively equivalent with representative ones.