Efficient Specialization of Relational Concepts
CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND PSYCHOLOGY PROJECT
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An algorithm is presented for a common induction problem, the specialization of overly general concepts. A concept is too general when it matches a negative example. The particular case addressed here assumes that concepts are represented as conjunctions of positive literals, that specialization is performed by conjoining literals to the overly general concept, and that the resulting specializations are to be as general as possible. Although the problem is NP-hard, there exists an algorithm, based on manipulation of bit vectors, that has provided good performance in practice. Keywords Strategy discovery Skill acquisition Machine learning Cognitive science Impasses-driven learning Artificial intelligence.