Pac-learning non-recursive Prolog clauses
Artificial Intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Top-down induction of first-order logical decision trees
Artificial Intelligence
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning Structurally Indeterminate Clauses
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Detecting Traffic Problems with ILP
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
RSD: relational subgroup discovery through first-order feature construction
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
An improved society of hill-climbers and its application on batch process scheduling
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Short communication: A new relational learning system using novel rule selection strategies
Knowledge-Based Systems
Feature Discovery with Type Extension Trees
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
An efficient approximation to lookahead in relational learners
ECML'06 Proceedings of the 17th European conference on Machine Learning
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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.