Generalizing Atoms in Constraint Logic
This paper studies the generalization of atomic formulas, or atoms, that are augmented with constraints on or among their terms. The atoms may also be viewed as definite clauses whose antecedents express the constraints. Atoms are generalized relative to a body of background information about the constraints. The paper first examines generalization of atoms with only monadic constraints. The paper develops an algorithm for the generalization task and discusses algorithm complexity. It then extends the algorithm to apply to atoms with constraints of arbitrary arity. The paper also presents semantic properties of the generalizations computed by the algorithms, making the algorithms applicable to such problems as abduction, induction, and knowledge base vivification. The paper emphasizes the application to induction and presents a pac-learning result for constrained atoms.
