Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using explanation-based and empirical methods in theory revision
Using explanation-based and empirical methods in theory revision
Probabilistic revision of logical domain theories
Probabilistic revision of logical domain theories
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Machine Learning
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Data-Driven Theory Refinement Using KBDistAl
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Theoretical Computer Science
On automatic knowledge validation for Bayesian knowledge bases
Data & Knowledge Engineering
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The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the "flow" of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair flawed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories.