Identifying precedents under uncertainty

  • A. de Korvin
  • G. Quirchmayr
  • S. Hashemi
Legal Systyems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 856)


Information about the case to be decided rarely is complete and precise. So dealing with imprecise information definitely is one of the major issues of legal decision making. In order to be able to identify a non-empty set of precedents most similar to our case, we introduce the Dempster-Shafer rule for combining information from independent sources and use the resulting mass functions to determine the importance of each precedent in our knowledge system. Additionally, the method is illustrated by an example.


Legal Decision Making Information Retrieval in Law Accessing Precedents Fuzzy Logic and Legal Decision Making 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [de Korvin et al. 1992]
    A. de Korvin, R. Kleyle, R. Lea, The object recognition problem when features fail to be homogeneous, International Journal of Approximate Reasoning 1993; 8:141–162.Google Scholar
  2. [de Korvin 1993 et al.]
    A. de Korvin, G. Quirchmayr, S. Hashemi, Legal decision making under uncertainty, in Proceedings of Expersys-93 (H.S. Nwana, T. Martelli, Eds.), i.i.t.t, 1993: 50–67.Google Scholar
  3. [Kleyle and de Korvin 1994]
    R. Kleyle and A. de Korvin, Two methods for object identification with imprecise information. To be published in 1994.Google Scholar
  4. [Giles 1982]
    R. Giles, Foundations for a theory of possibility, in Fuzzy Information and Decision Processes (M. M. Gupta and E. Sanchez, Eds.), North Holland Publishing Co., 193–195, 1982.Google Scholar
  5. [Gordon and Quirchmayr 1987]
    T. F. Gordon and G. Quirchmayr, Der Einsatz der Modellierungssprache OBLOG zum Entwurf von Juristischen Expertensystemen im Wege des Prototyping am Besipiel eines Modells des Verfahrens der Eidesstattlichen Versicherung, Springer IFB 143, 137–154, Berlin 1987.Google Scholar
  6. [Jaffray 1990]
    J.-Y. Jaffray, Application of Linear utility theory to belief functions, in Uncertainty in Artificial Intelligence, Vol. 5, (Max Henrion, Ed.), New York, Elsevier Publishing Co., New York, 1–8, 1990.Google Scholar
  7. [Marsh and Soulsby 1987]
    S. B. Marsh and J. Soulsby, Outlines of English Law, McGraw-Hill, London, 124 ff., 1987.Google Scholar
  8. [Reiter 1978]
    R. Reiter, On Reasoning by Default, Proceedings of the 2nd Symposium on Theoretical Issues in Natural Language Processing, Urbana, Illinois.Google Scholar
  9. [Smetz 1990]
    P. Smetz, Belief functions versus probability functions, in Uncertainty in Artificial Intelligence, Vol. 5, (Max Henrion, Ed.), New York, Elsevier Publishing Co., New York, 1–8, 1990.Google Scholar
  10. [Strat 1990]
    T. M. Strat, Decision analysis using belief functions, Intern. J. Approximate Reasoning 4, 391–417, 1990.Google Scholar
  11. [Susskind 1987]
    R. Susskind, Expert Systems in Law, Oxford University Press 1987.Google Scholar
  12. [Zebda 1984]
    A. Zebda, The Investigation of Cost Variances: A Fuzzy Set Theory Approach, Vol. 15, 1984, 359–388. Decision sciences.Google Scholar
  13. [Yager 1986]
    R. Yager, A general approach to decision making with evidential knowledge. Uncertainty in Artificial Intelligence (L. N. Kanal and J. F. Lemmer, Eds.), North Holland, Amsterdam, 317–327, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • A. de Korvin
    • 1
  • G. Quirchmayr
    • 2
  • S. Hashemi
    • 1
  1. 1.University of Houston-DowntownHoustonUSA
  2. 2.Institut f. Angewandte Informatik und InformationssystemeUniversität WienWienAustria

Personalised recommendations