Introduction to artificial intelligence
Introduction to artificial intelligence
Automatic knowledge base refinement for classification systems
Artificial Intelligence
Incremental version-space merging: a general framework for concept learning
Incremental version-space merging: a general framework for concept learning
Proceedings of the sixth international workshop on Machine learning
Finding new rules for incomplete theories: explicit biases for induction with contextual information
Proceedings of the sixth international workshop on Machine learning
Augmenting domain theory for explanation-based generalisation
Proceedings of the sixth international workshop on Machine learning
Combining explanation-based learning and artificial neural networks
Proceedings of the sixth international workshop on Machine learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Machine Learning
Abductive Explanation in Text Understanding: Some Problems and Solutions
Abductive Explanation in Text Understanding: Some Problems and Solutions
Detecting and correcting errors of omission after explanation-based learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Online Randomization Strategies to Obfuscate User Behavioral Patterns
Journal of Network and Systems Management
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This paper presents a comprehensive approach to automatic theory refinement. In contrast to other systems, the approach is capable of modifying a theory which contains multiple faults and faults which occur at intermediate points in the theory. The approach uses explanations to focus the corrections to the theory, with the corrections being supplied by an inductive component. In this way, an attempt is made to preserve the structure of the original theory as much as possible. Because the approach begins with an approximate theory, learning an accurate theory takes fewer examples than a purely inductive system. The approach has application in expert system development, where an initial, approximate theory must be refined. The approach also applies at any point in the expert system lifecycle when the expert system generates incorrect results. The approach has been applied to the domain of molecular biology and shows significantly better results then a purely inductive learner.