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Learning Agents’ Relations in Interactive Multiagent Dynamic Influence Diagrams

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Agents and Data Mining Interaction (ADMI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9145))

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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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Authors and Affiliations

  1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China

    Yinghui Pan & Hua Mao

  2. Department of Automation, Xiamen University, Xiamen, China

    Yinghui Pan, Yifeng Zeng & Hua Mao

  3. School of Computing, Teesside University, Middlesbrough, UK

    Yinghui Pan & Hua Mao

  4. College of Computer Science, Sichuan University, Chengdu, China

    Yinghui Pan & Hua Mao

Authors
  1. Yinghui Pan
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  2. Yifeng Zeng
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  3. Hua Mao
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Corresponding author

Correspondence to Yifeng Zeng .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  2. Teesside University, Middlesborough, United Kingdom

    Yifeng Zeng

  3. Nanyang Technological University, Singapore, Singapore

    Bo An

  4. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Andreas L. Symeonidis

  5. Russian Academy of Sciences, St. Petersburg, Russia

    Vladimir Gorodetsky

  6. University of Liverpool, Liverpool, United Kingdom

    Frans Coenen

  7. University of Illinois at Chicago, Chicago, Illinois, USA

    Philip S. Yu

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20229-7

  • Online ISBN: 978-3-319-20230-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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