Journal of the ACM (JACM)
Towards inductive generalisation in higher order logic
ML92 Proceedings of the ninth international workshop on Machine learning
MFPS '92 Selected papers of the meeting on Mathematical foundations of programming semantics. Part II : lambda calculus and domain theory: lambda calculus and domain theory
Logic and Learning
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Probabilistic modelling, inference and learning using logical theories
Annals of Mathematics and Artificial Intelligence
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Learning first-order recursive theories remains a difficult learning task in a normal Inductive Logic Programming (ILP) setting, although numerous approaches have addressed it; using Higher-order Logic (HOL) avoids having to learn recursive clauses for such a task. It is one of the areas where Higher-order Logic Learning (HOLL), which uses the power of expressivity of HOL, can be expected to improve the learnability of a problem compared to First-order Logic Learning (FOLL). We present a first working implementation of ?Progol, a HOLL system adapting the ILP system Progol and the HOL formalism ?Prolog, which was introduced in a poster last year [15]. We demonstrate that ?Progol outperforms standard Progol when learning first-order recursive theories, by improving significantly the predictive accuracy of several worked examples, especially when the learning examples are small with respect to the size of the data.