Abstract
Solving interactive multiagent decision making problems is a challenging task since it needs to model how agents interact over time. From individual agents’ perspective, interactive dynamic influence diagrams (I-DIDs) provide a general framework for sequential multiagent decision making in uncertain settings. Most of the current I-DID research focuses on the setting of \(n=2\) agents, which limits its general applications. This paper extends I-DIDs for \(n>2\) agents, which as expected increases the solution complexity due to the model space of other agents in the extended I-DIDs. We exploit data of agents’ interactions to discover their relations thereby reducing the model complexity. We show preliminary results of the proposed techniques in one problem domain.
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Acknowledgment
This research was supported by the Nature Science Foundation of Jiangxi Province, China (No. 20132BAB211026), and the Research Foundation of Education Bureau of Jiangxi Province, China (No. GJJ12741 and No. GJJ13306). The Project was also supported by the National Natural Science Foundation of China (No. 61375070 and No. 61402306).
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Pan, Y., Zeng, Y., Mao, H. (2015). Learning Agents’ Relations in Interactive Multiagent Dynamic Influence Diagrams. In: Cao, L., et al. Agents and Data Mining Interaction. ADMI 2014. Lecture Notes in Computer Science(), vol 9145. Springer, Cham. https://doi.org/10.1007/978-3-319-20230-3_1
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DOI: https://doi.org/10.1007/978-3-319-20230-3_1
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