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Semantics of multiply sectioned Bayesian networks for cooperative multi-agent distributed interpretation

  • Knowledge Representation III: Agents
  • Conference paper
  • First Online:
Book cover Advances in Artifical Intelligence (Canadian AI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1081))

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Abstract

In order to represent cooperative multi-agents who must reason with uncertain knowledge, a coherent framework is necessary. We choose multiply sectioned Bayesian networks (MSBNs) as the basis for this study because they are based on well established theory on Bayesian networks and because they are modular. In this paper, we focus on the semantics of a MSBN-based multi-agent system (MAS) for cooperative distributed interpretation. In particular, we establish the conditions under which the joint probability distribution of a MSBN-based MAS can be meaningfully interpreted. These conditions imply that a coherent MSBN-based MAS can be constructed using agents built by different developers. We show how the conditions can be satisfied technically under such a context.

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, S4S OA2, Regina, Sask., Canada

    Y. Xiang

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  1. Y. Xiang
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Gordon McCalla

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© 1996 Springer-Verlag Berlin Heidelberg

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Xiang, Y. (1996). Semantics of multiply sectioned Bayesian networks for cooperative multi-agent distributed interpretation. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_53

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  • DOI: https://doi.org/10.1007/3-540-61291-2_53

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61291-9

  • Online ISBN: 978-3-540-68450-3

  • eBook Packages: Springer Book Archive

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