Learning Horn Expressions with LOGAN-H

Marta Arias; Roni Khardon; Jérôme Maloberti

The paper introduces LOGAN-H — a system for learning first-order function-free Horn expressions from interpretations. The system is based on an algorithm that learns by asking questions and that was proved correct in previous work. The current paper shows how the algorithm can be implemented in a practical system, and introduces a new algorithm based on it that avoids interaction and learns from examples only. The LOGAN-H system implements these algorithms and adds several facilities and optimizations that allow efficient applications in a wide range of problems. As one of the important ingredients, the system includes several fast procedures for solving the subsumption problem, an NP-complete problem that needs to be solved many times during the learning process. We describe qualitative and quantitative experiments in several domains. The experiments demonstrate that the system can deal with varied problems, large amounts of data, and that it achieves good classification accuracy.

Learning Horn Expressions with LOGAN-H

Abstract