Case inference in resolution-based languages

  • T. Wakayama
  • T. H. Payne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 310)


Informally, case inference is a type of inference that inherently involves disjunctions in deriving definite consequences. We show that a difficulty with efficient implementation of case inference in resolution-based languages stems from the fact that case inference always requires derived clauses to be reused as side clauses: in general, the number of derived clauses is quite large, and storing all of them seems unacceptably inefficient in programming language settings. However, our results also show that in retrieving definite information, this use of derived clauses is necessary only when case inference is required. This in turn leads to our next finding that storing a relatively small class of derived clauses, which is characterized in terms of certain properties of case inference, is sufficient for proving all definite consequences. We then show that a conservative approximation of this class can be, in effect, precomputed for clause sets not containing purely negative clauses.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Chang, C.L., and Lee, R.C.T., Symbolic Logic and Mechanical Theorem Proving, Academic Press, New York, 1973.Google Scholar
  2. [2]
    Grant,J., and Minker,J., Answering Queries in Indefinite Databases and the Null Value Problem. Technical Report 1374, University of Maryland, 1983.Google Scholar
  3. [3]
    Gallaire,H., Minker,J., and Nicolas,J-M., Logic and Databases: A deductive Approach. Computing Surveys, Vol.16, No. 2, 1984.Google Scholar
  4. [4]
    Imielinski, T., and Lipski, W., On Representing Incomplete Information in a Relational Database. Proceedings of the 7th International Conference on Very Large Databases. IEEE, New York, 1981.Google Scholar
  5. [5]
    Kowalski,R., and Kuehner,D., Linear Resolution with Selection Function, Artificial Intelligence 2, 1971.Google Scholar
  6. [6]
    Lloyd,J.W., Foundations of Logic Programming, Springer-Verlag, 1984.Google Scholar
  7. [7]
    Loveland,D.W., Mechanical Theorem Proving by Model Elimination. J. ACM, 1968.Google Scholar
  8. [8]
    Loveland,D.W., Near Horn Prolog, 4th International Conference in Logic Programming, Melbourne, 1987.Google Scholar
  9. [9]
    Minker, J., On Indefinite Databases and the Closed World Assumption. Lecture Notes in Computer Science, No. 138, Springer-Verlag, New York, 1982.Google Scholar
  10. [10]
    Pryzymusinski,T., On the Semantics of Stratified Deductive Databases, Foundations of Deductive Databases and Logic Programming, J. Minker. ed., 1987.Google Scholar
  11. [11]
    Shoenfield, J., Mathematical Logic. Addison-Wesley, Reading, Mass. 1967.Google Scholar
  12. [12]
    Stickel, M.E., A Prolog Technology Theorem Prover: Implementation by an Extended Prolog Compiler. Lecture Notes in Computer Science, No 230, Springer-Verlag, New York, 1986.Google Scholar
  13. [13]
    Wakayama,T., and Payne,T.H., Refining Linear Resolution for Clause Sets with Disjunctive Heads, in preparation.Google Scholar
  14. [14]
    Yahya,A., and Henschen,L., Deduction in Non-Horn Databases, J. of Automated Reasoning 1, 1985.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • T. Wakayama
    • 1
  • T. H. Payne
    • 2
  1. 1.School of Computer and Information ScienceSyracuse UniversitySyracuse
  2. 2.Department of Mathematics and Computer ScienceUniversity of CaliforniaRiverside

Personalised recommendations