On the complexity of cutting-plane proofs
Discrete Applied Mathematics
An analysis of first-order logics of probability
Artificial Intelligence
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Using the Groebner basis algorithm to find proofs of unsatisfiability
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Journal of the ACM (JACM)
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Learning to Reason with a Restricted View
Machine Learning
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Information and Computation
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Simplified and improved resolution lower bounds
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Non-automatizability of bounded-depth frege proofs
Computational Complexity
Machine Learning
Autodidactic learning and reasoning
Autodidactic learning and reasoning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Towards understanding and harnessing the potential of clause learning
Journal of Artificial Intelligence Research
Learning to reason the non monotonic case
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
Partial observability and learnability
Artificial Intelligence
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
On the power of clause-learning SAT solvers as resolution engines
Artificial Intelligence
Clause-learning algorithms with many restarts and bounded-width resolution
Journal of Artificial Intelligence Research
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
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We consider the problem of how enormous databases of "common sense" knowledge can be both learned and utilized in reasoning in a computationally efficient manner. We propose that this is possible if the learning only occurs implicitly, i.e., without generating an explicit representation. We show that it is feasible to invoke such implicitly learned knowledge in essentially all natural tractable reasoning problems. This implicit learning also turns out to be provably robust to occasional counterexamples, as appropriate for such common sense knowledge.