This paper presents a method for learning logic programs without explicit negative examples by exploiting an assumption of output completeness. A mode declaration is supplied for the target predicate and each training input is assumed to be accompanied by all of its legal outputs. Any other outputs generated by an incomplete program implicitly represent negative examples; however, large numbers of ground negative examples never need to be generated. This method has been incorporated into two ILP systems, CHILLIN and IFOIL, both of which use intensional background knowledge. Tests on two natural language acquisition tasks, case-role mapping and past-tense learning, illustrate the advantages of the approach.

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In Proceedings of the Fifth International Workshop on Inductive Logic Programming (ILP-95), pp. 403-416, Leuven, Belgium 1995.

@InProceedings{zelle:ilp95, title={Inducing Logic Programs without Explicit Negative Examples}, author={John M. Zelle and Cynthia A. Thompson and Mary Elaine Califf and Raymond J. Mooney}, booktitle={Proceedings of the Fifth International Workshop on Inductive Logic Programming (ILP-95)}, address={Leuven, Belgium}, pages={403-416}, url="http://www.cs.utexas.edu/users/ai-lab?zelle:ilp95", year={1995} }