Distributed multi-agent probabilistic reasoning with Bayesian networks

  • Yang Xiang
Communications Knowledge Representation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 869)


Main stream approaches in distributed artificial intelligence (DAI) are essentially logic-based. Little has been reported to explore probabilistic approach in DAI. On the other hand, Bayesian networks have been applied to many AI tasks that require reasoning under uncertainty. However, as commonly applied, a single-agent paradigm is assumed in Bayesian networks.

This paper extends multiply sectioned Bayesian networks (MSBNs) for single-agent systems into a framework for multi-agent distributed interpretation systems. Each cooperative agent is represented as a Bayesian subnet that consumes its own computational resource, gathers its own evidence, and can answer queries. Unlike in single-agent systems where evidence is entered one subnet at a time, multiple agents may acquire evidence in parallel. We add to the set of single-agent MSBN operations new belief propagation operations which, when performed, regain global consistency. Due to the inter-agent ’distance’ and the associated communication cost, global consistency can not be maintained constantly. We show that, when the proposed operations are followed, between two successive communications, the answers to queries from an agent are consistent with all local evidence gathered so far, and are consistent with all global evidence gathered up to the last communication.


knowledge representation and integration approximate reasoning probabilistic reasoning Bayesian networks distributed artificial intelligence distributed reasoning 


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  1. 1.
    A.H. Bond and L. Gasser. An analysis of problems and research in dai. In A.H. Bond and L. Gasser, editors, Readings in Distributed Artificial Intelligence, Morgan Kaufmann, 3–35, 1988.Google Scholar
  2. 2.
    A.H. Bond and L. Gasser, editors. Readings in Distributed Artificial Intelligence. Morgan Kaufmann, 1988.Google Scholar
  3. 3.
    L. Gasser and M.N. Huhns, editors. Distributed Artificial Intelligence, Volume II. Morgan Kaufmann, 1989.Google Scholar
  4. 4.
    F.V. Jensen, S.L. Lauritzen, and K.G. Olesen. Bayesian updating in causal probabilistic networks by local computations. Comput. Stat. Quarterly, (4):269–282, 1990.Google Scholar
  5. 5.
    F.V. Jensen, K.G. Olesen, and S.K. Andersen. An algebra of bayesian belief universes for knowledge-based systems. Networks, 20:637–659, 1990.Google Scholar
  6. 6.
    S.L. Lauritzen and D.J. Spiegelhalter. Local computation with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B, (50):157–244, 1988.Google Scholar
  7. 7.
    V.R. Lesser and L.D. Erman. Distributed interpretation: a model and experiment. IEEE Transactions on Computers, C-29(12):1144–1163, 1980.Google Scholar
  8. 8.
    R.E. Neapolitan. Probabilistic Reasoning in Expert Systems. John Wiley, 1990.Google Scholar
  9. 9.
    J. Pearl. Fusion, propagation, and structuring in belief networks. Artificial Intelligence, (29):241–288, 1986.Google Scholar
  10. 10.
    J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.Google Scholar
  11. 11.
    Y. Xiang, B. Pant, A. Eisen, M. P. Beddoes, and D. Poole. Multiply sectioned bayesian networks for neuromuscular diagnosis. Artificial Intelligence in Medicine, 5:293–314, 1993.Google Scholar
  12. 12.
    Y. Xiang, D. Poole, and M. P. Beddoes. Multiply sectioned bayesian networks and junction forests for large knowledge based systems. Computational Intelligence, 9(2):171–220, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

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

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