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Belief network algorithms: A study of performance based on domain characterisation

  • Reasoning with Changing and Incomplete Information
  • Conference paper
  • First Online:
Learning and Reasoning with Complex Representations (PRICAI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1359))

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Abstract

In recent years belief networks have become a popular representation for reasoning with incomplete and changing information and are used in a wide variety of applications. There are a number of exact and approximate inference algorithms available for performing belief updating, however in general the task is NP-hard. Typically comparisons are made of only a few algorithms, and on a particular example network. We survey belief network algorithms and propose a system for domain characterisation as a basis for algorithm comparison. We present performance results using this framework from three sets of experiments: (1) on the Likelihood Weighting (LW) and Logic Sampling (LS) stochastic simulation algorithms? (2) on the performance of LW and Jensen's algorithms on state-space abstracted networks, (3) some comparisons of the time performance of LW, LS and the Jensen algorithm. Our results indicate that domain characterisation can be useful for predicting inference algorithm performance on a belief network for a new application domain.

Topic indicator: Experimental studies of inference algorithms.

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References

  1. S.K. Andersen, K.G. Olesen, F.V. Jensen, and F. Jensen. HUGIN — a shell for building Bayesian belief universes for expert systems. In Proc. of IJCAI-89, Detroit, MI, 1989.

    Google Scholar 

  2. I. Beinlich, H. Suermondt, R. Chavez, and G. Cooper. The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proc. of the 2nd European Conf. on AI in medicine, pages 689–693, 1992.

    Google Scholar 

  3. K-C. Chang and R. Fung. Refinement and coarsening of bayesian networks. In Proc. of UAI-90, pages 475–482, 1990.

    Google Scholar 

  4. Homer L. Chin and Gregory F. Cooper. Bayesian belief network inference using simulation. In Uncertainty in Artificial Intelligence 3, pages 129–147, 1989.

    Google Scholar 

  5. G.F. Cooper. The computational complexity of probabilistic inference using bayesian belief networks. Artificial Intelligence, 42:393–405, 1990.

    Article  MATH  MathSciNet  Google Scholar 

  6. P. Dagum and M. Luby. Approximating probabilistic inference in belief networks is NP-hard. Artificial Intelligence, pages 141–153, 1993.

    Google Scholar 

  7. B. D'Ambrosio. Incremental probabilistic inference. In Proc. of UAI-93, pages 301–308, 1993.

    Google Scholar 

  8. T. Dean and M. Boddy. An analysis of time-dependent planning. In Proceedings AAAI-88, pages 49-54. AAAI, 1988.

    Google Scholar 

  9. T. Dean and M. P. Wellman. Planning and control. Morgan Kaufman Publishers, San Mateo, Ca., 1991.

    Google Scholar 

  10. D. Draper. Localized Partial Evaluation of Belief Networks. PhD thesis, Dept. of Computer Science, U. of Washington, 1995.

    Google Scholar 

  11. D. L. Draper and S. Hanks. Localized partial evaluation of a belief network. In Proc. of UAI-94, pages 170–177, 1994.

    Google Scholar 

  12. M. Henrion. Propagating uncertainty in bayesian networks by logic sampling. In J. Lemmer and L. Kanal, editors, Proc. of UAI-88, pages 149–163. North Holland, Amsterdam, 1988.

    Google Scholar 

  13. E.J. Horvitz, H.J. Suermondt, and G.F. Cooper. Bounded conditioning: Flexible inference for decisions under scarce resources. In Proc. of UAI-89, pages 182–193, 1989.

    Google Scholar 

  14. Thomas Hrycej. Gibbs sampling in bayesian networks. In Artificial Intelligence, pages 351–363, 1990.

    Google Scholar 

  15. M. Hulme. Improved sampling for diagnostic reasoning in bayesian networks. In Proc. of UAI-95, pages 315–322, 1995.

    Google Scholar 

  16. F. Jensen and S.K. Andersen. Approximations in Bayesian belief universes for knowledge based systems. In Proc. of UAI-90, 1990.

    Google Scholar 

  17. K. Kanazawa, D. Koller, and S. Russell. Stochastic simulation algorithms for dynamic probabilistic networks. In Proc. of UAI-95, pages 346–351, 1995.

    Google Scholar 

  18. J. Kirman. Predicting real-time planner performance by domain characterization. PhD thesis, Brown Univ., 1994.

    Google Scholar 

  19. U. Kjaerulff. Reduction of computation complexity in bayesian networks through removal of weak dependencies. In Proc. of UAI-94, pages 374–382, 1994.

    Google Scholar 

  20. S.L. Lauritzen and D.J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, 50(2):157–224, 1988.

    MATH  MathSciNet  Google Scholar 

  21. C. Liu and M. Wellman. On state-space abstraction for anytime evaluation of bayesian networks. In IJCAI 95: Anytime Algorithms and Deliberation Scheduling Workshop, pages 91–98, 1995.

    Google Scholar 

  22. A.E. Nicholson and N. Jitnah. Belief network algorithms: a study of performance using domain characterisation. Technical Report 96/249, Dept. of Computer Science, Monash University, 1996.

    Google Scholar 

  23. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.

    Google Scholar 

  24. D. Poole. Average-case analysis of a search algorithm for estimating prior and posterior probabilities in bayesian networks with extreme probabilities. In Proc. of IJCAI-93, pages 606–612, 1993.

    Google Scholar 

  25. M. Pradham, G. Provan, B. Middleton, and M. Henrion. Knowledge engineering for large belief networks. In Proc. of UAI-94, pages 484–490, 1994.

    Google Scholar 

  26. R. Shachter and M. Peot. Evidential reasoning using likelihood weighting. Personal Communication, 1989.

    Google Scholar 

  27. R. Shachter and M. Peot. Simulation approaches to general probabilistic inference on belief networks. In Proc. of UAI-89, pages 311–318, 1989.

    Google Scholar 

  28. Sampath Srinivas and Jack Breese. Ideal: Influence diagram evaluation and analysis in lisp. Technical Report No. 23, Rockwell International Science Center, 1989.

    Google Scholar 

  29. Michael P. Wellman and Chao-Lin Liu. State-space abstraction for anytime evaluation of probabilistic networks. In Proc. of UAI-94, pages 567–574, 1994.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Monash University, 3168, Clayton, VIC, Australia

    N. Jitnah & A. E. Nicholson

Authors
  1. N. Jitnah
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  2. A. E. Nicholson
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Editor information

Grigoris Antoniou Aditya K. Ghose Mirosław Truszczyński

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© 1998 Springer-Verlag Berlin Heidelberg

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Jitnah, N., Nicholson, A.E. (1998). Belief network algorithms: A study of performance based on domain characterisation. In: Antoniou, G., Ghose, A.K., Truszczyński, M. (eds) Learning and Reasoning with Complex Representations. PRICAI 1996. Lecture Notes in Computer Science, vol 1359. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-64413-X_35

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  • DOI: https://doi.org/10.1007/3-540-64413-X_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64413-2

  • Online ISBN: 978-3-540-69780-0

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