Saturation, nonmonotonic reasoning and the closed-world assumption
Artificial Intelligence
On the relationship between circumscription and negation as failure
Artificial Intelligence
Induction as nonmonotonic inference
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Linear resolution for consequence finding
Artificial Intelligence
Machine Learning - special issue on inductive logic programming
Logical settings for concept-learning
Artificial Intelligence
Abduction from logic program: semantics and complexity
Theoretical Computer Science
Nonmonotonic Logic II: Nonmonotonic Modal Theories
Journal of the ACM (JACM)
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Answer set programming and plan generation
Artificial Intelligence
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
The Many Faces of Inductive Logic Programming
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
On Indefinite Databases and the Closed World Assumption
Proceedings of the 6th Conference on Automated Deduction
Induction as Consequence Finding
Machine Learning
Rationality postulates for induction
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Induction from answer sets in nonmonotonic logic programs
ACM Transactions on Computational Logic (TOCL)
Logic programs with monotone abstract constraint atoms*
Theory and Practice of Logic Programming
Exploring relations between answer set programs
Logic programming, knowledge representation, and nonmonotonic reasoning
Integrating model checking and inductive logic programming
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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This paper introduces a novel logical framework for concept-learning called brave induction. Brave induction uses brave inference for induction and is useful for learning from incomplete information. Brave induction is weaker than explanatory induction which is normally used in inductive logic programming, and is stronger than learning from satisfiability, a general setting of concept-learning in clausal logic. We first investigate formal properties of brave induction, then develop an algorithm for computing hypotheses in full clausal theories. Next we extend the framework to induction in nonmonotonic logic programs. We analyze computational complexity of decision problems for induction on propositional theories. Further, we provide examples of problem solving by brave induction in systems biology, requirement engineering, and multiagent negotiation.