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Learning Prolog programs from examples

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44846

Krishna Rao,  M. R. K.
Programming Logics, MPI for Informatics, Max Planck Society;

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Zitation

Krishna Rao, M. R. K. (1996). Learning Prolog programs from examples. In K. Anjaneyulu, M. Sasikumar, & S. Ramani (Eds.), Knowledge Based Computer Systems (pp. 19-30). New Delhi, India: Narosa.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-ABE4-9
Zusammenfassung
Logic programs with elegant and simple declarative semantics have become very common in many areas of artificial intelligence such as knowledge acquisition, knowledge representation and common sense and legal reasoning. For example, in Human GENOME project, logic programs are used in the analysis of amino acid sequences, protein structure and drug design etc. In this paper, we investigate the problem of learning logic (Prolog) programs from examples and present an inference algorithm for a class of programs. This class of programs (called one-recursive programs) is based on the divide-and-conquer approach and mode/type annotations. Our class is very rich and includes many programs from Sterling and Shapiro's book including {\tt append, merge, split, insert, insertion-sort, preorder} and {\tt inorder} traversal of binary trees, polynomial recognition, derivatives, sum of a list of natural numbers etc., whereas earlier results can only deal with very simple programs without local variables and at most two clauses and one predicate \cite{colt92}. Further, our algorithm does not need examples for auxiliary predicates, but only for the target predicate.