A Value Equivalence Approach for Solving Interactive Dynamic Influence Diagrams

Ross Conroy, Yifeng Zeng, Marc Cavazza, Jing Tang, Yinghui Pan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

Interactive dynamic influence diagrams (I-DIDs) are recognized graphical models for sequential multiagent decision making under uncertainty. They represent the problem of how a subject agent acts in a common setting shared with other agents who may act in sophisticated ways. The difficulty in solving I-DIDs is mainly due to an exponentially growing space of candidate models ascribed to other agents over time. in order to minimize the model space, the previous I-DID techniques prune behaviorally equivalent models. In this paper, we challenge the minimal set of models and propose a value equivalence approach to further compress the model space. The new method reduces the space by additionally pruning behaviorally distinct models that result in the same expected value of the subject agent's optimal policy. To achieve this, we propose to learn the value from available data particularly in practical applications of real-time strategy games. We demonstrate the performance of the new technique in two problem domains.

Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems
Place of PublicationNew York, NY
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Pages1162–1170
Number of pages9
ISBN (Electronic)9781450342391
Publication statusPublished - May 2016
Externally publishedYes

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