Advantages of decision lists and implicit negatives in Inductive Logic Programming |
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Authors: | Mary Elaine Califf Raymond J Mooney |
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Affiliation: | (1) Department of Computer Sciences, University of Texas at Austin, 78712 Austin, TX, USA |
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Abstract: | This paper demonstrates the capabilities offoidl, 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 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 thatfoidl’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 allowfoidl to learn correct programs from far fewer examples thanfoil.
This research was supported by a fellowship from AT&T awarded to the first author and by the National Science Foundation under
grant IRI-9310819.
Mary Elaine Califf: She is currently pursuing her doctorate in Computer Science at the University of Texas at Austin where she is supported
by a fellowship from AT&T. Her research interests include natural language understanding, particularly using machine learning
methods to build practical natural language understanding systems such as information extraction systems, and inductive logic
programming.
Raymond Joseph Mooney: He is an Associate Professor of Computer Sciences at the University of Texas at Austin. He recerived his Ph.D. in Computer
Science from the University of Illinois at Urbana-Champaign in 1988. His current research interests include applying machine
to natural language understanding, inductive logic programming, knowledge-base and theory refinement, learning for planning,
and learning for recommender systems. He serves on the editorial boards of the journalNew Generation Computing, theMachine Learning journal, theJournal of Artificial Intelligence Research, and the journalApplied Intelligence. |
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Keywords: | Inductive Logic Programming Machine Learning Decision Lists |
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