Skip to main content
Springer Nature Link
Log in

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

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

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

  • 148 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  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. A.H. Bond and L. Gasser, editors. Readings in Distributed Artificial Intelligence. Morgan Kaufmann, 1988.

    Google Scholar 

  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. L. Gasser and M.N. Huhns, editors. Distributed Artificial Intelligence, Volume II. Morgan Kaufmann, 1989.

    Google Scholar 

  5. C. Ghezzi, M. Jazayeri, and D. Mandrioli. Fundamentals of Software Engineering Prentice Hall, 1991.

    Google Scholar 

  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. 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. 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. R.E. Neapolitan. Probabilistic Reasoning in Expert Systems. John Wiley and Sons, 1990.

    Google Scholar 

  10. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.

    Google Scholar 

  11. D. Poole, A. Mackworth, and R. Goebel. Computational Intelligence: A Logical Approach. Oxford University Press, forthcoming, 1996.

    Google Scholar 

  12. W.A. Shay. Understanding Data Communications and Networks. PWS Publishing, 1995.

    Google Scholar 

  13. A. Silberschatz and P.B. Galvin. Operating System Concepts. Addison Wesley, 1994.

    Google Scholar 

  14. M. Wooldridge and N.R. Jennings. Intelligent agents: theory and practice. Knowledge Engineering Review, 10(2):115–152, 1995.

    Google Scholar 

  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. 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. 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. Y. Xiang. Distributed structure verification in multiply sectioned bayesian networks. In To appear in Proc. Florida AI Research Symposium, 1996.

    Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

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

    Y. Xiang

Authors
  1. Y. Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Gordon McCalla

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61291-9

  • Online ISBN: 978-3-540-68450-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics