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An Implementation of Belief Change Operations Based on Probabilistic Conditional Logic

  • Marc Finthammer
  • Christoph Beierle
  • Benjamin Berger
  • Gabriele Kern-Isberner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5753)

Abstract

Probabilistic conditionals are a powerful means for expressing uncertain knowledge. In this paper, we describe a system implemented in Java performing probabilistic reasoning at optimum entropy. It provides nonmonotonic belief change operations like revision and update and supports advanced querying facilities including diagnosis and what-if-analysis.

Keywords

Probabilistic Reasoning Epistemic State Belief Change Probabilistic Conditional Junction Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Paris, J., Vencovska, A.: In defence of the maximum entropy inference process. International Journal of Approximate Reasoning 17(1), 77–103 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Kern-Isberner, G.: Characterizing the principle of minimum cross-entropy within a conditional-logical framework. Artificial Intelligence 98, 169–208 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Shore, J.: Relative entropy, probabilistic inference and AI. In: Uncertainty in Artificial Intelligence, pp. 211–215. North-Holland, Amsterdam (1986)CrossRefGoogle Scholar
  4. 4.
    Finthammer, M., Beierle, C., Berger, B., Kern-Isberner, G.: Probabilistic reasoning at optimum entropy with the MEcore system. In: Proceedings 22nd International FLAIRS Conference, FLAIRS 2009. AAAI Press, Menlo Park (2009)Google Scholar
  5. 5.
    Berger, B.: Realisierung einer Komponente für die Revision probabilistischen Wissens. BSc Thesis, Dep. of Computer Science, FernUniversität Hagen (2008)Google Scholar
  6. 6.
    Csiszár, I.: I-divergence geometry of probability distributions and minimization problems. Ann. Prob. 3, 146–158 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Rödder, W., Reucher, E., Kulmann, F.: Features of the expert-system-shell SPIRIT. Logic Journal of the IGPL 14(3), 483–500 (2006)CrossRefzbMATHGoogle Scholar
  8. 8.
    Teh, Y.W., Welling, M.: On improving the eciency of the iterative proportional fitting procedure. In: Proc. of the Ninth International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marc Finthammer
    • 1
  • Christoph Beierle
    • 1
  • Benjamin Berger
    • 1
  • Gabriele Kern-Isberner
    • 2
  1. 1.Dept. of Computer ScienceFernUniversität in HagenHagenGermany
  2. 2.Dept. of Computer ScienceTU DortmundDortmundGermany

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