Communications of the ACM
Using explanation-based and empirical methods in theory revision
Using explanation-based and empirical methods in theory revision
Multistrategy Learning and Theory Revision
Machine Learning - Special issue on multistrategy learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
RUTH: an ILP Theory Revision System
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
On theory revision with queries
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
More theory revision with queries (extended abstract)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
The Automated Refinement of a Requirements Domain Theory
Automated Software Engineering
Theory Revision with Queries: DNF Formulas
Machine Learning
Theory revision with queries: horn, read-once, and parity formulas
Artificial Intelligence
Problem solving with insufficient resources
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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In this paper we consider the problem of theory patching, in which we are given a domain theory, some of whose components are indicated to be possibly flawed, and a set of labeled training examples for the domain concept. The theory patching problem is to revise only the indicated components of the theory, such that the resulting theory correctly classifies all the training examples. Theory patching is thus a type of theory revision in which revisions are made to individual components of the theory. Our concern in this paper is to determine for which classes of logical domain theories the theory patching problem is tractable. We consider both propositional and first-order domain theories, and show that the theory patching problem is equivalent to that of determining what information contained in a theory is stable regardless of what revisions might be performed to the theory. We show that determining stability is tractable if the input theory satisfies two conditions: that revisions to each theory component have monotonic effects on the classification of examples, and that theory components act independently in the classification of examples in the theory. We also show how the concepts introduced can be used to determine the soundness and completeness of particular theory patching algorithms.