This paper presents a method for inducing logic programs from examples that learns a new class of concepts called first-order decision lists, defined as ordered lists of clauses each ending in a cut. The method, called FOIDL, is based on FOIL but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as learning the past-tense of English verbs, a task widely studied in the context of the symbolic/connectionist debate. FOIDL is able to learn concise, accurate programs for this problem from significantly fewer examples than previous methods (both connectionist and symbolic).
Journal of Artificial Intelligence Research, Vol. 3 (1995), pp. 1-24.

Mary Elaine Califf Ph.D. Alumni mecaliff [at] ilstu edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu