ACCELERATING SEARCH IN FUNCTION INDUCTION

Abstract

Inducing functions from examples is an important requirement in many learning systems. Blind search is the most general approach, but is vastly less efficient than specialized problem-solving methods. This paper presents a new strategy to accelerate search without sacrificing generality. Experiments with numeric functions show several orders of magnitude performance increase over the standard search technique. Two factors account for this improvement. First, the new strategy manipulates functions in groups instead of singly, so that many can be selected or discarded with only one comparison. Second, functional equivalence is handled automatically by the internal organization of search space.