Communications of the ACM
Foundations of logic programming
Foundations of logic programming
A Prolog technology theorem prover: implementation by an extended Prolog computer
Journal of Automated Reasoning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Guiding induction with domain theories
Machine learning
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
Towards inductive generalisation in higher order logic
ML92 Proceedings of the ninth international workshop on Machine learning
Sub-unification: a tool for efficient induction of recursive programs
ML92 Proceedings of the ninth international workshop on Machine learning
Compression, significance and accuracy
ML92 Proceedings of the ninth international workshop on Machine learning
Interactive Concept-Learning and Constructive Induction by Analogy
Machine Learning
An operator-based approach to first-order theory revision
An operator-based approach to first-order theory revision
Algorithmic Program DeBugging
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
ECML '93 Proceedings of the European Conference on Machine Learning
Bayesian inductive logic programming
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Machine Learning - special issue on inductive logic programming
Machine Learning
Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach
Applied Intelligence
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
A Note on Two Simple Transformations for Improving the Efficiency of an ILP System
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
An empirical study of the use of relevance information in inductive logic programming
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
The Knowledge Engineering Review
Word Sense Disambiguation Using Inductive Logic Programming
Inductive Logic Programming
The Journal of Machine Learning Research
A phenotypic genetic algorithm for inductive logic programming
Expert Systems with Applications: An International Journal
Parallel ILP for distributed-memory architectures
Machine Learning
USP-IBM-1 and USP-IBM-2: the ILP-based systems for lexical sample WSD in SemEval-2007
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Language Resources and Evaluation
A study of applying dimensionality reduction to restrict the size of a hypothesis space
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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Inductive Logic Programming (ILP) is a research area which investigates the construction of first-order definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of real-world domains. These include the learning of structure-activity rules for drug design, finite-element mesh design rules, rules for primary-secondary prediction of protein structure and fault diagnosis rules for satellites. There is a well established tradition of learning-in-the-limit results in ILP. Recently some results within Valiant's PAC-learning framework have also been demonstrated for ILP systems. In this paper it is argued that algorithms can be directly derived from the formal specifications of ILP. This provides a common basis for Inverse Resolution, Explanation-Based Learning, Abduction and Relative Least General Generalisation. A new general-purpose, efficient approach to predicate invention is demonstrated. ILP is underconstrained by its logical specification. Therefore a brief overview of extra-logical constraints used in ILP systems is given. Some present limitations and research directions for the field are identified.