This paper demonstrates the capabilities of FOIDL, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use of intensional background knowledge. The development of FOIDL was originally motivated by the problem of learning to generate the past tense of English verbs; however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show that FOIDL's decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow FOIDL to learn correct programs from far fewer examples than FOIL.

PS

New Generation Computing, Vol. 16, 3 (1998), pp. 263-281.

@Article{califf:ngc98, title={Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming}, author={Mary Elaine Califf and Raymond J. Mooney}, volume={16}, journal={New Generation Computing}, number={3}, pages={263-281}, url="http://www.cs.utexas.edu/users/ai-lab?califf:ngc98", year={1998} }