Prolog by example: how to learn, teach and use it
Prolog by example: how to learn, teach and use it
Test case generation by means of learning techniques
SIGSOFT '93 Proceedings of the 1st ACM SIGSOFT symposium on Foundations of software engineering
Flattening and Saturation: Two Representation Changes for Generalization
Machine Learning - Special issue on evaluating and changing representation
Algorithmic Program DeBugging
Logic Program Synthesis and Transformation: Proceedings of LOPSTR '92, International Workshop on Logic Program Synthesis and Trasformation, University of Manchester, 2-3 July, 1992
Learning Logical Definitions from Relations
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
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As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.