A theory of diagnosis from first principles
Artificial Intelligence
SOAR: an architecture for general intelligence
Artificial Intelligence
Model-based reasoning: troubleshooting
Exploring artificial intelligence
Learning in the presence of partial explanations
Information and Computation
Learning read-once formulas with queries
Journal of the ACM (JACM)
Characterizing diagnoses and systems
Artificial Intelligence
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
A formal model of hierarchical concept learning
Information and Computation
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Knowledge-based artificial neural networks
Artificial Intelligence
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Learning in the presence of finitely or infinitely many irrelevant attributes
Journal of Computer and System Sciences
An algorithm to learn read-once threshold formulas, and transformations between learning models
Computational Complexity
Learning recursive functions from approximations
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Attribute-efficient learning in query and mistake-bound models
Journal of Computer and System Sciences
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
Algorithmic Program DeBugging
Switching and Finite Automata Theory: Computer Science Series
Switching and Finite Automata Theory: Computer Science Series
Machine Learning
Machine Learning
Optimal Attribute-Efficient Learning of Disjunction, Parity and Threshold Functions
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
Improved Algorithms for Theory Revision with Queries
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Tractability of theory patching
Journal of Artificial Intelligence Research
Theory revision with queries: horn, read-once, and parity formulas
Artificial Intelligence
The Journal of Machine Learning Research
Theoretical Computer Science
Projective DNF formulae and their revision
Discrete Applied Mathematics
| Hi-index | 0.00 |
The theory revision, or concept revision, problem is to correct a given, roughly correct concept. This problem is considered here in the model of learning with equivalence and membership queries. A revision algorithm is considered efficient if the number of queries it makes is polynomial in the revision distance between the initial theory and the target theory, and polylogarithmic in the number of variables and the size of the initial theory. The revision distance is the minimal number of syntactic revision operations, such as the deletion or addition of literals, needed to obtain the target theory from the initial theory. Efficient revision algorithms are given for three classes of disjunctive normal form expressions: monotone k-DNF, monotone m-term DNF and unate two-term DNF. A negative result shows that some monotone DNF formulas are hard to revise.