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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)

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.

Keywords

Knowledge representation probabilistic reasoning multi-agent systems 

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References

  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, pages 3–35. Morgan Kaufmann, 1988.Google Scholar
  2. 2.
    A.H. Bond and L. Gasser, editors. Readings in Distributed Artificial Intelligence. Morgan Kaufmann, 1988.Google Scholar
  3. 3.
    A.P. Dawid and S.L. Lauritzen. Hyper markov laws in the statistical analysis of decomposable graphical models. Annals of Statistics, 21(3):1272–1317, 1993.Google Scholar
  4. 4.
    L. Gasser and M.N. Huhns, editors. Distributed Artificial Intelligence, Volume II. Morgan Kaufmann, 1989.Google Scholar
  5. 5.
    C. Ghezzi, M. Jazayeri, and D. Mandrioli. Fundamentals of Software Engineering Prentice Hall, 1991.Google Scholar
  6. 6.
    F.V. Jensen, S.L. Lauritzen, and K.G. Olesen. Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly, (4): 269–282, 1990.Google Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    R.E. Neapolitan. Probabilistic Reasoning in Expert Systems. John Wiley and Sons, 1990.Google Scholar
  10. 10.
    J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.Google Scholar
  11. 11.
    D. Poole, A. Mackworth, and R. Goebel. Computational Intelligence: A Logical Approach. Oxford University Press, forthcoming, 1996.Google Scholar
  12. 12.
    W.A. Shay. Understanding Data Communications and Networks. PWS Publishing, 1995.Google Scholar
  13. 13.
    A. Silberschatz and P.B. Galvin. Operating System Concepts. Addison Wesley, 1994.Google Scholar
  14. 14.
    M. Wooldridge and N.R. Jennings. Intelligent agents: theory and practice. Knowledge Engineering Review, 10(2):115–152, 1995.Google Scholar
  15. 15.
    Y. Xiang. Distributed multi-agent probabilistic reasoning with bayesian networks. In Z.W. Ras and M. Zemankova, editors, Methodologies for Intelligent Systems, pages 285–294. Springer-Verlag, 1994.Google Scholar
  16. 16.
    Y. Xiang. Distributed scheduling of multiagent systems. In Proc. 1st International Conf. on Multi-agent Systems, pages 390–397, San Francisco, CA, 1995.Google Scholar
  17. 17.
    Y. Xiang. Optimization of inter-subnet belief updating in multiply sectioned bayesian networks. In Proc. Eleventh Conference on Uncertainty in Artificial Intelligence, pages 565–573, Montreal, Quebec, 1995.Google Scholar
  18. 18.
    Y. Xiang. Distributed structure verification in multiply sectioned bayesian networks. In To appear in Proc. Florida AI Research Symposium, 1996.Google Scholar
  19. 19.
    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
  20. 20.
    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 1996

Authors and Affiliations

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

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