Making believers out of computers
Artificial Intelligence
Learning from good and bad data
Learning from good and bad data
An incremental method for generating prime implicants/implicates
Journal of Symbolic Computation
Linear resolution for consequence finding
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
Artificial Intelligence
ICLP '02 Proceedings of the 18th International Conference on Logic Programming
Applying SAT Methods in Unbounded Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Hypotheses Finding via Residue Hypotheses with the Resolution Principle
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
A Bounded Search Space of Clausal Theories
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Induction as Consequence Finding
Machine Learning
On computing all abductive explanations from a propositional Horn theory
Journal of the ACM (JACM)
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SOLAR: An automated deduction system for consequence finding
AI Communications - Practical Aspects of Automated Reasoning
Found ations of assumption-based truth maintenance systems: preliminary report
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
Reasoning with characteristic models
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Inverse subsumption for complete explanatory induction
Machine Learning
Efficient conflict analysis for finding all satisfying assignments of a boolean circuit
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Logic programming for Boolean networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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This paper investigates the problem of computing hypotheses in disjunctive normal form (DNF) for explanatory induction. This is contrasted to the usual setting of ILP, where hypotheses are obtained in conjunctive normal form (CNF), i.e., a set of clauses. We present two approaches to compute DNF hypotheses as well as several sound and complete algorithms. This problem naturally contains abduction from clausal theories, and can be related to model-based inductive reasoning, in which propositional reasoning methods such as SAT techniques and prime implicant computation can be utilized.