Learning by explanation of failures
The EBG learning technique has been mainly used in learning processes based on positive examples and successful experiences. However, several authors have demonstrated that failed proofs revealed to be quite useful as a form of avoiding future failures. The first attempts to learn from failure were based on the axiomatization of the problem-solver and on the creation of a specific meta-theory for all possible failures. Whenever there is a positive example of a failure, EBG is used to make operational the meta-theory.
Siqueira & Puget designed a new technique with a different philosophy to learn from counter-examples using only the domain theory. Their method finds a sufficient generalized condition from the failed proof of a goal. EBGF is still a fragile and incomplete technique as it doesn't cover all cases. The failure of a proof has specific characteristics which are not considered when we deal with positive proofs. In this paper we show the weaknesses of EBGF and we propose an improved technique to learn from failures in the presence of a counter-example. Our method is implemented in Prolog and its efficiency is currently under analysis.
KeywordsExplanation based learning failure
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