Sub-unification: a tool for efficient induction of recursive programs
ML92 Proceedings of the ninth international workshop on Machine learning
Algorithmic Program DeBugging
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Learning Logical Definitions from Relations
Machine Learning
LIME: A System for Learning Relations
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Tow-down Induction of Logic Programs from Incomplete Samples
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Normal Programs and Multiple Predicate Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
ILP with noise and fixed example size: a Bayesian approach
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
| Hi-index | 0.00 |
Within the empirical ILP setting we propose a method of inducing definite programs from examples -- even when those examples are incomplete and occasionally incorrect. This system, named NRMIS, is a top-down batch learner that can make use of intensional background knowledge and learn programs involving multiple target predicates. It consists of three components: a generalization of Shapiro's contradiction backtracing algorithm; a heuristic guided search of refinement graphs; and a LIME-like theory evaluator. Although similar in spirit to MIS, NRMIS avoids its dependence on an oracle while retaining the expressiveness of a hypothesis language that allows recursive clauses and function symbols. NRMIS is tested on domains involving noisy and sparse data. The results illustrate NRMIS's ability to induce accurate theories in all of these situations.