Categories and Subject Descriptors: I.2.3 [Artificial Intelligence]: Deduction and Theorem Proving -- deduction; I.2.4 [Artificial Intelligence]: Knowledge Representation Formalisms and Methods; I.2.6 [Artificial Intelligence]: Learning -- Knowledge acquisition
General Terms: Algorithms, Theory
Additional Key Words and Phrases: Common sense reasoning, computational learning, knowledge representation, model-based reasoning
Selected references
- Dana Angluin. Computational learning theory: Survey and selected bibliography. In Proceedings of the Twenty-Fourth Annual ACM Symposium on the Theory of Computing, pages 351-369, Victoria, British Columbia, Canada, 4-6 May 1992.
- Nader H. Bshouty. Exact learning Boolean functsion via the monotone theory. Information and Computation, 123(1):146-153, 15 November 1995.
- Jeffrey Jackson. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. In 35th Annual Symposium on Foundations of Computer Science, pages 42-53, Santa Fe, New Mexico, 20-22 November 1994. IEEE.
- Michael Kearns and Ming Li. Learning in the presence of malicious errors (extended abstract). In Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, pages 267-280, Chicago, Illinois, 2-4 May 1988.
- Christos H. Papadimitriou. On selecting a satisfying truth assignment (extended abstract). In 32nd Annual Symposium on Foundations of Computer Science, pages 163-169, San Juan, Puerto Rico, 1-4 October 1991. IEEE.
- Bart Selman and Henry Kautz. Knowledge compilation and theory approximation. Journal of the ACM, 43(2):193-224, March 1996.
- Leslie G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, November 1984.
- L. G. Valiant and V. V. Vazirani. NP is as easy as detecting unique solutions. Theoretical Computer Science, 47(1):85-93, 1986.