DLAB: A declarative language bias formalism

  • Luc Dehaspe
  • Luc De Raedt
Communications Session 7B Learning and Discovery Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1079)


We describe the principles and functionalities of DLAB (Declarative LAnguage Bias). DLAB can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional formulae, association rules, Horn clauses, or full clauses. A Prolog implementation of DLAB is available by ftp access.


declarative language bias concept learning knowledge discovery 


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  1. 1.
    H. Adé, L. De Raedt, and M. Bruynooghe. Declarative Bias for Specific-to-General ILP Systems. Machine Learning, 20(1/2):119–154, 1995.Google Scholar
  2. 2.
    F. Bergadano and D. Gunetti. An interactive system to learn functional logic programs. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1044–1049. Morgan Kaufmann, 1993.Google Scholar
  3. 3.
    W.F. Clocksin and C.S. Mellish. Programming in Prolog. Springer-Verlag, Berlin, 1981.Google Scholar
  4. 4.
    W.W. Cohen. Grammatically biased learning: learning logic programs using an explicit antecedent description language. Artificial Intelligence, 68:303–366, 1994.Google Scholar
  5. 5.
    L. De Raedt. Interactive Theory Revision: an Inductive Logic Programming Approach. Academic Press, 1992.Google Scholar
  6. 6.
    L. Dehaspe and L. De Raedt. DLAB: a declarative language bias for concept learning and knowledge discovery engines. Technical Report CW-214, Department of Computer Science, Katholieke Universiteit Leuven, October 1995.Google Scholar
  7. 7.
    B. Dolsak, I. Bratko, and A. Jezernik. Finite element mesh design: An engineering domain for ilp application. In S. Wrobel, editor, Proceedings of the 4th International Workshop on Inductive Logic Programming, volume 237 of GMD-Studien, Sankt Augustin, Germany, 1994. Gesellschaft für Mathematik und Datenverarbeitung MBH.Google Scholar
  8. 8.
    M. Genesereth and N. Nilsson. Logical foundations of artificial intelligence. Morgan Kaufmann, 1987.Google Scholar
  9. 9.
    D. Gordon and M. desJardins. Evaluation and selection of biases in machine learning. Machine Learning, 20(1/2):5–22, 1995.Google Scholar
  10. 10.
    J-U. Kietz and S. Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. In S. Muggleton, editor, Inductive logic programming, pages 335–359. Academic Press, 1992.Google Scholar
  11. 11.
    N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.Google Scholar
  12. 12.
    J.W. Lloyd. Foundations of logic programming. Springer-Verlag, 2nd edition, 1987.Google Scholar
  13. 13.
    I.G. MacDonald. Symmetric functions and Hall polynomials. Clarendon Oxford, 1979.Google Scholar
  14. 14.
    T.M. Mitchell. The need for biases in learning generalizations. Technical Report CBM-TR-117, Department of Computer Science, Rutgers University, 1980.Google Scholar
  15. 15.
    T.M.Mitchell. Generalization as search. Artificial Intelligence, 18:203–226, 1982.Google Scholar
  16. 16.
    S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the 1st conference on algorithmic learning theory, pages 368–381. Ohmsma, Tokyo, Japan, 1990.Google Scholar
  17. 17.
    C. Nédellec, H. Adé, and B. Bergadano, F. a nd Tausend. Declarative bias in ILP. In L. De Raedt, editor, Advances in Inductive Logic Programming, volume 32 of Frontiers in Artificial Intelligence and Applica tions, pages 82–103. IOS Press, 1996.Google Scholar
  18. 18.
    S. Russell and B. Grosof. A Declarative Approach to Bias in Concept Learning. In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI87), pages 505–510, 1987.Google Scholar
  19. 19.
    Leon Sterling and Ehud Shapiro. The art of Prolog. The MIT Press, 1986.Google Scholar
  20. 20.
    P.E. Utgoff. Shift of bias for inductive concept-learning. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: an artificial intelligence approach, pages 107–148. Morgan Kaufmann, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Luc Dehaspe
    • 1
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium

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