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About this product Product Information Inductive Logic Programming is a research area situated in machine learning and logic programming, two subfields of artificial intelligence. The goal of inductive logic programming is to develop theories, techniques and tools for inducing hypotheses from observations using the representations from computational logic.
This book provides a detailed state-of-the-art overview of Inductive Logic Programming as well as a collection of recent technical contributions to Inductive Logic Programming. It highlights some of the most important recent results within Inductive Logic Programming and can be used as a thorough introduction to the field. This book is relevant to students, researchers and practitioners of artificial intelligence and computer science, especially those concerned with machine learning, data mining and computational logic.
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- Discovering H-bonding rules in crystals with inductive logic programming. - Abstract - Europe PMC.
Muggleton, S. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited.
Inductive logic programming - Wikipedia
Machine Learning, 1. Cropper, A. Learning efficient logical robot strategies involving composable objects. Logical minimisation of meta-rules within meta-interpretive learning. LNAI Oltean, M.
Evolving winning strategies for nim-like games. World Computer Congress, 9 2.
Winning Ways for your Mathematical Plays, Volume 1. Corapi, D. Inductive logic programming as abductive search.
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