Version:1.0 Alpha
Code:Quintus Prolog
References:Mooney and Califf 1995

FOIDL is a descendant of FOIL differing from its predecessor in the following three ways. First, FOIDL is able to process intensionally defined background knowledge. Second, the requirement to provide explicit negative examples can be replaced by the assumption of output completeness. The output completeness assumption requires a mode declaration identifying input and output arguments of the target predicate. Output completeness then states that for every unique input pattern appearing in the training set, all correct output patterns occur in the examples in the training set. Together with the mode declaration, the positive examples then implicitly determine the negative examples. The third difference between FOIDL and FOIL is that FOIDL, as FFOIL, induces decision lists. Unlike FFOIL, FOIDL generates the clauses in the decision list in reverse order, that is, clauses learnt first appear at the end of the decision list. As the covering algorithm tends to learn more general clauses covering many positive examples first the more general clauses are placed as default cases at the end of the decision list.


  1. R.J. Mooney and M.E. Califf. Induction of first-order decision lists: Results on learning the past tense of English verbs. Journal of Artificial Intelligence Research, 3:1-24, 1995.

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