Learning regular sets from queries and counterexamples
Information and Computation
Towards a theory of declarative knowledge
Foundations of deductive databases and logic programming
Negation as failure using tight derivations for general logic programs
Foundations of deductive databases and logic programming
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Theory refinement combining analytical and empirical methods
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
Exact learning Boolean functions via the monotone theory
Information and Computation
Attribute-efficient learning in query and mistake-bound models
Journal of Computer and System Sciences
The complexity of theory revision
Artificial Intelligence
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Theory Revision with Queries: DNF Formulas
Machine Learning
Quasi-Acyclic Propositional Horn Knowledge Bases: Optimal Compression
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Machine Learning
Learning Acyclic First-Order Horn Sentences from Entailment
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Belief revision via Lamarckian evolution
New Generation Computing
Preference Elicitation and Query Learning
The Journal of Machine Learning Research
Theory revision with queries: horn, read-once, and parity formulas
Artificial Intelligence
On computing all abductive explanations from a propositional Horn theory
Journal of the ACM (JACM)
Learning and verifying quantified boolean queries by example
Proceedings of the 32nd symposium on Principles of database systems
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A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the measured resource) is polynomial in the syntactic distance between the initial and the target concept, but only polylogarithmic in the number of variables in the universe. We give efficient revision algorithms in the model of learning with equivalence and membership queries. The algorithms work in a general revision model where both deletion and addition revision operators are allowed. In this model one of the main open problems is the efficient revision of Horn formulas. Two revision algorithms are presented for special cases of this problem: for depth-1 acyclic Horn formulas, and for definite Horn formulas with unique heads.