History:

The FOIL algorithm (Quinlan and Cameron-Jones 1993) takes as input a set of binary data R (separated by spaces) and produces a set of RACs. The resulting classifier, as generated by the LUCS-KDD FOIL algorithm described herein, comprises a linked list of rules ordered according to their Laplace precision (Clark and Boswell 1991). First-order rule-learning sets: FOIL (Quinlan, 1990): FOIL is similar to the learning approach of the propositional rule, except for the following: • FOIL accepts the rules of first order and must therefore take into account the variables in the prerequisites of the rule. • FOIL uses a special performance measure (FOIL-GAIN) which takes into account different variable bindings. • FOILS only look for predictable rules when the target is literal (True or false). • FOIL performs a simple climbing search rather than a beam search.

Inductive logic programming (ILP) is an increasing subtheme of machine learning that studies the induction of Prolog programs from examples in the presence of basic knowledge (Muggleton, 1992, Lavrac and Dzeroski, 1994). Because of the expressivity of first-order logic, ILP methods can learn relational and recursive concepts that can not be represented in the attribute / value representations assumed by most machine learning algorithms [3]. ILP methods have succeeded in inducing small sorting and list manipulation programs (Shapiro 1983, Sammut and Banerji 1986, Muggleton and Buntine 1988, Quinlan and Cameron Jones 1993) and encouraging results on important applications such as prediction of proteins. Secondary structure (Muggleton, King and Sternberg, 1992) and automating the construction of parsers in natural language (Zelle and Mooney, 1994b), However, current ILP techniques make important assumptions that limit their application. Here are three common assumptions: 1. Basic knowledge is provided as an extension in the form of a set of literals on the ground. 2. Explicit negative examples of the target predicate are available. 3. The target program is expressed in \ pure "Prolog where the order of the clauses is irrelevant and the procedural operators such as cut (!) Are forbidden The ILP systems currently the most known and most successful [6], Golem (Muggleton & Feng, 1990) and Foil (Quinlan, 1990), both make these three assumptions, but each of these assumptions provides important limitations since:

1. An adequate extensional representation of basic knowledge is often infinite or intractably large.
2. Explicit negative examples are often unavailable and an adequate set of negative examples calculated using a closed world hypothesis is infinite or intractably large.
3. The concise representation of many concepts requires the use of ordering clauses and / or cuts (Bergadano, Gunetti and Trinchero, 1993).