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Construction of Privacy Preserving Hypertree Agent Organization as Distributed Maximum Spanning Tree

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Advances in Artificial Intelligence (Canadian AI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7884))

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Abstract

Decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning are some of the essential tasks of a multiagent system (MAS). Many frameworks exist for these tasks, and a number of them organize agents into a junction tree (JT). Although these frameworks all reap benefits of communication efficiency and inferential soundness from the JT organization, their potential capacity on agent privacy has not been realized fully. The contribution of this work is a general approach to construct the JT organization through a maximum spanning tree (MST), and a new distributed MST algorithm, that preserve agent privacy on private variables, shared variables and agent identities.

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Authors and Affiliations

  1. School of Computer Science, University of Guelph, Canada

    Yang Xiang & Kamala Srinivasan

Authors
  1. Yang Xiang
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  2. Kamala Srinivasan
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Editor information

Editors and Affiliations

  1. University of Alberta, Edmonton, AB, Canada

    Osmar R. Zaïane

  2. Department of Computer Science, University of Regina, Canada

    Sandra Zilles

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Xiang, Y., Srinivasan, K. (2013). Construction of Privacy Preserving Hypertree Agent Organization as Distributed Maximum Spanning Tree. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_17

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  • DOI: https://doi.org/10.1007/978-3-642-38457-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38456-1

  • Online ISBN: 978-3-642-38457-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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