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

  • Y. Xiang
Knowledge Representation III: Agents
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1081)


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.


Knowledge representation probabilistic reasoning multi-agent systems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Y. Xiang
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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