This paper presents a general framework, learning search-control heuristics for logic programs, which can be used to improve both the efficiency and accuracy of knowledge-based systems expressed as definite-clause logic programs. The approach combines techniques of explanation-based learning and recent advances in inductive logic programming to learn clause-selection heuristics that guide program execution. Two specific applications of this framework are detailed: dynamic optimization of Prolog programs (improving efficiency) and natural language acquisition (improving accuracy). In the area of program optimization, a prototype system, DOLPHIN, is able to transform some intractable specifications into polynomial-time algorithms, and outperforms competing approaches in several benchmark speedup domains. A prototype language acquisition system, CHILL, is also described. It is capable of automatically acquiring semantic grammars, which uniformly incorprate syntactic and semantic constraints to parse sentences into case-role representations. Initial experiments show that this approach is able to construct accurate parsers which generalize well to novel sentences and significantly outperform previous approaches to learning case-role mapping based on connectionist techniques. Planned extensions of the general framework and the specific applications as well as plans for further evaluation are also discussed.

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unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.

@unpublished{zelle:proposal93, title={Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition}, author={John M. Zelle}, month={March}, pages={36 pages}, note={Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?zelle:proposal93", year={1993} }