Efficient multiple predicate learner based on fast failure mechanism

Abstract

We present a multiple predicate learner (MPL-Core) which efficiently induces some Horn clauses from example sets of multiple predicates and relative background knowledge. Core, a single predicate learning module, has a fast failure mechanism, and can select refinement operators based on the learning task. By means of GPC, an efficient pruning method, Core effectively prunes unpromising branches in a search tree, making the search space a rational volume. MPL-Core employs both the intensional and extensional learning style in the induction of target predicates. Furthermore, our system with the fast failure mechanism gives a distinct improvement over the existing multiple predicate learning systems in the computational complexity.