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

Learning Relations: Basing Top-Down Methods on Inverse Resolution

Abstract Top-down algorithms for relational learning specialize general rules until they are consistent, and are guided by heuristics of different kinds. In general, a correct solution is not guaranteed. By contrast, bottom-up methods are well formalized, usually within the framework of inverse resolution. Inverse resolution has also been used as an efficient tool for deductive reasoning, and here we prove that input refutations can be translated into inverse unit refutations. This result allows us to show that top-down learning methods can be also described by means of inverse resolution, yielding a unified theory of relational learning

Free-text keystroke dynamics authentication for Arabic language

This study introduces an approach for user authentication using free-text keystroke dynamics which incorporates text in Arabic language. The Arabic language has completely different characteristics to those of English. The approach followed in this study involves the use of the keyboard's key-layout. The method extracts timing features from specific key-pairs in the typed text. Decision trees were exploited to classify each of the users' data. In parallel for comparison, support vector machines were also used for classification in association with an ant colony optimisation feature selection technique. The results obtained from this study are encouraging as low false accept rates and false reject rates were achieved in the experimentation phase. This signifies that satisfactory overall system performance was achieved by using the typing attributes in the proposed approach, while typing Arabic text