LOGICAL SETTINGS FOR CONCEPT-LEARNING

Three different formalizations of concept-learning in logic (as well a s some variants) are analyzed and related. It is shown that learning f rom interpretations reduces to learning from entailment, which in rum reduces to learning from satisfiability. The implications of this resu lt for inductive logic programming and computational learning theory a re then discussed, and guidelines for choosing a problem-setting are f ormulated. (C) 1997 Elsevier Science B.V.