Advances in Inductive Logic Programming
Luc de Raedt
IOS Press, 1996 - Computers - 323 pages
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. Inductive Logic Programming has a high potential for applications in data mining, automated scientific discovery, knowledge discovery in databases, as well as automatic programming. 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. The state-of-the-art overview is based on - among others - the successful ESPRIT basic research project no. 6020 on Inductive Logic Programming, funded by the European Commission from 1992 till 1995. 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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The Inductive Logic Programming Project
First Order Theory Refinement
Predicate Invention in Inductive Logic Programming
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abducible predicates abductive explanations abductive theory algorithm applications approach argument arity Artificial Intelligence background knowledge background predicates background theory base clause Bergadano Bratko clause set Computer concept Conference on Machine CRUSTACEAN decision tree defined derivation disjoint union domain domain theory Dzeroski encoding extensionally covers FOIL framework given GOLEM heuristic Horn clauses hypothesis space ILP project ILP systems inductive learning Inductive Logic Programming input integrity constraints International Workshop Katholieke Universiteit Leuven language bias Lavrac learnability learning system Machine Learning method Morgan Kaufmann Muggleton negation as failure negative examples negative literals operator output polynomial positive and negative positive examples predicate invention Proc Prolog propositional Raedt recursive clause recursive definitions recursive theory refinement relation representation RIPPER rules saturations search bias semantics set of clauses SKILit Stephen Muggleton stochastic structure target predicate techniques theory revision TRACY"0 training examples training set Workshop on Inductive