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Assumption-based modeling using ABEL

  • B. Anrig
  • R. Haenni
  • J. Kohlas
  • N. Lehmann
Accepted Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1244)

Abstract

Today, different formalisms exist to solve reasoning problems under uncertainty. For most of the known formalisms, corresponding computer implementations are available. The problem is that each of the existing systems has its own user interface and an individual language to model the knowledge and the queries.

This paper proposes ABEL, a new and general language to express uncertain knowledge and corresponding queries. Examples from different domains show that ABEL is powerful and general enough to be used as common modeling language for the existing software systems. A prototype of ABEL is implemented in Evidenzia, a system restricted to models based on propositional logic. A general ABEL solver is actually being implemented.

Keywords

Type Weather Numerical Argument Common LISP Interior Decoration Exist Software System 
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. Almond, R.G. 1990. Fusion and Propagation of Graphical Belief Models: an Implementation and an Example. Ph.D. thesis, Department of Statistics, Harvard University.Google Scholar
  2. Almond, R.G. 1995. Graphical Belief Modeling. Chapman and Hall.Google Scholar
  3. Andersen, S.K., Olesen, K.G., Jensen, F.V., & Jensen, F. 1990. HUGIN — a Shell for Building Bayesian Belief Universes for Expert Systems. Pages 332–338 of: Shafer, G., & Pearl, J. (eds), Readings in Uncertain Reasoning. Morgan Kaufmann.Google Scholar
  4. Anrig, B., Haenni, R., & Lehmann, N. 1997. ABEL — A New Language for Assumption-Based Evidential Reasoning under Uncertainty. Tech. Rep. 97–01. University of Fribourg, Institute of Informatics.Google Scholar
  5. Davis, R. 1984. Diagnostic Reasoning based on Structure and Behaviour. Artificial Intelligence, 24, 347–410.Google Scholar
  6. de Kleer, J. 1986. An Assumption-based TMS. Artificial Intelligence, 28, 127–162.Google Scholar
  7. de Kleer, J. & Williams, B.C. 1987. Diagnosing Multiple Faults. Artificial Intelligence, 32, 97–130.Google Scholar
  8. Haenni, R. 1996. Propositional Argumentation Systems and Symbolic Evidence Theory. Ph.D. thesis, Institute of Informatics, University of Fribourg.Google Scholar
  9. Hsia, Y.T., & Shenoy, P.P. 1989. An Evidential Language for Expert Systems. Pages 9–14 of: Ras, Z.W. (ed), Methodologies for Intelligent Systems. North-Holland.Google Scholar
  10. Kohlas, J., & Monney, P.A. 1993. Probabilistic Assumption-Based Reasoning. In: Heckerman, & Mamdani (eds), Proc. 9th Conf. on Uncertainty in Artificial Intelligence. Kaufmann, Morgan Publ.Google Scholar
  11. Kohlas, J., & Monney, P.A. 1995. A Mathematical Theory of Hints. An Approach to the Dempster-Shafer Theory of Evidence. Lecture Notes in Economics and Mathematical Systems, vol. 425. Springer.Google Scholar
  12. Kohlas, J., Monney, P.A., Anrig, B., & Haenni, R. 1996. Model-Based Diagnostics and Probabilistic Assumption-Based Reasoning. Tech. Rep. 96-09. University of Fribourg, Institute of Informatics.Google Scholar
  13. Lauritzen, S.L., & Spiegelhalter, D.J. 1988. Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems. Journal of Royal Statistical Society, 50(2), 157–224.Google Scholar
  14. Lehmann, N. 1994. Entwurf und Implementation einer annahmenbasierten Sprache. Diplomarbeit. Institute of Informatics, University of Fribourg.Google Scholar
  15. Reiter, R. 1987. A Theory of Diagnosis From First Principles. Artificial Intelligence, 32, 57–95.Google Scholar
  16. Saffiotti, A., & Umkehrer, E. 1991. PULCINELLA: A General Tool for Propagating Uncertainty in Valuation Networks. Tech. Rep. IRIDIA, Université de Bruxelles.Google Scholar
  17. Shafer, G. 1976. The Mathematical Theory of Evidence. Princeton University Press.Google Scholar
  18. Shenoy, P.P. 1995. Binary Join Trees. Tech. Rep. 270. School of Business, University of Kansas.Google Scholar
  19. Shenoy, P.P., & Shafer, G. 1990. Axioms for Probability and Belief Functions Propagation. In: Shachter, R.D., & al. (eds), Uncertainty in Artificial Intelligence 4. North Holland.Google Scholar
  20. Srinivas, S., & Breese, J. 1990. IDEAL: A Software Package for Analysis of Influence Diagrams. In: Proceedings of the Sixth Uncertainty Conference in AI, Cambridge, MA.Google Scholar
  21. Steele, G. L. 1990. Common Lisp — the Language. 2d edn. Digital Press.Google Scholar
  22. Xu, H., & Kennes, R. 1994. Steps Toward Efficient Implementation of Dempster-Shafer Theory. Pages 153–174 of: Yager, R.R., Fedrizzi, M., & Kacprzyk, J. (eds), Advances in the Dempster-Shafer Theory of Evidence. John Wiley and Sons.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • B. Anrig
    • 1
  • R. Haenni
    • 1
  • J. Kohlas
    • 1
  • N. Lehmann
    • 1
  1. 1.Institute of InformaticsUniversity of FribourgFribourgSwitzerland

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