A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning

Abstract

Hill-climbing search is the most commonly used search algorithm in ILP systems because it permits the generation of theories in short running times. However, a well known drawback of this greedy search strategy is its myopia. Macro-operators (or macros for short), a recently proposed technique to reduce the search space explored by exhaustive search, can also be argued to reduce the myopia of hill-climbing search by automatically performing a variable-depth look-ahead in the search space. Surprisingly, macros have not been employed in a greedy learner. In this paper, we integrate macros into a hill-climbing learner. In a detailed comparative study in several domains, we show that indeed a hill-climbing learner using macros performs significantly better than current state-of-the-art systems involving other techniques for reducing myopia, such as fixed-depth look-ahead, template-based look-ahead, beam-search, or determinate literals. In addition, macros, in contrast to some of the other approaches, can be computed fully automatically and do not require user involvement nor special domain properties such as determinacy.