Double compression of models for interactive dynamic influence diagrams

J. Luo, B. Li, Yifeng Zeng

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Interactive Dynamic Influence Diagrams(I-DIDs) constitute a graphic model for multi-agent decision making under uncertainty, but solving them is provably intractable. 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, but composing behavioral equivalence classes is a complex process as we need to compare all solutions of possible models of other agents in the merge operation. To further simplify the calculation, this paper describes an approximate solution of I-DIDs based on double compression method. First of time, using the insight that beliefs that are spatially close are likely to be behaviorally equivalent, cluster the models of other agents and select representative models from each cluster, and then, update those modes using the principle of discriminative behavior updates. We discuss the complexity of the approximation technique and demonstrate its empirical performance.
Original languageEnglish
Title of host publicationINISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications
Number of pages5
PublisherIEEE Press
Publication date1 Jan 2011
Pages188-192
ISBN (Print)9781612849195
DOIs
Publication statusPublished - 1 Jan 2011
EventInternational Symposium on Innovations in Intelligent Systems and Applications - Istanbul, Turkey
Duration: 15 Jun 201118 Jun 2011

Conference

ConferenceInternational Symposium on Innovations in Intelligent Systems and Applications
Country/TerritoryTurkey
CityIstanbul
Period15/06/201118/06/2011

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