The Difficulties of Learning Logic Programs with Cut

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.Comment: See http://www.jair.org/ for any accompanying file

Generalization of predicates with string arguments

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2002.Thesis (Master's) -- Bilkent University, 2002.Includes bibliographical references leaves 60-63.String/sequence generalization is used in many different areas such as machine learning, example-based machine translation and DNA sequence alignment. In this thesis, a method is proposed to find the generalizations of the predicates with string arguments from the given examples. Trying to learn from examples is a very hard problem in machine learning, since finding the global optimal point to stop generalization is a difficult and time consuming process. All the work done until now is about employing a heuristic to find the best solution. This work is one of them. In this study, some restrictions applied by the SLGG (Specific Least General Generalization) algorithm, which is developed to be used in an example-based machine translation system, are relaxed to find the all possible alignments of two strings. Moreover, a Euclidian distance like scoring mechanism is used to find the most specific generalizations. Some of the generated templates are eliminated by four different selection/filtering approaches to get a good solution set. Finally, the result set is presented as a decision list, which provides the handling of exceptional cases.Canıtezer, GökerM.S