Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
A logical framework for default reasoning
Artificial Intelligence
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Machine Learning - special issue on inductive logic programming
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Integrity Constraints in ILP Using a Monte Carlo Approach
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
Confirmation-Guided Discovery of First-Order Rules with Tertius
Machine Learning
Challenges for Inductive Logic Programming
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Representation of Incomplete Knowledge by Induction of Default Theories
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Automatic induction of abduction and abstraction theories from observations
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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A learning framework that combines the two frameworks of explanatory and descriptive Inductive Logic Programming (ILP) is presented. The induced hypotheses in this framework are pairs of the form (T, IC) where T is a definite clausal theory and IC is a set of integrity constraints. The two components allow us to combine complementary information from the same data by applying both explanatory and descriptive learning methods. This non-trivial integration is achieved using a nonmonotonic entailment relation for the basic notion of coverage in the combined language of rules and constraints where the constraints can restrict the conclusions derivable by the rules. We present a semantics for the new framework and then discuss different cases where combining information from explanatory and descriptive ILP could be useful. We present some basic algorithmic frameworks for learning in the new framework, and report on some preliminary experiments with encouraging results.