Learning to reason

Journal of the ACM

Volumn 44, Issue 5, 1997, Pages 697-725

  Khardon, Roni ^a   Roth, Dan ^a,b  

^a HARVARD UNIVERSITY   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

Common sense reasoning; Computational learning; Knowledge representation; Model based reasoning

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTERFACES (COMPUTER); KNOWLEDGE ACQUISITION; KNOWLEDGE REPRESENTATION; QUERY LANGUAGES;

COMMON SENSE REASONING; COMPUTATIONAL LEARNING; REASONING;

LEARNING ALGORITHMS;

EID: 0031224128     PISSN: 00045411     EISSN: None     Source Type: Journal    
DOI: 10.1145/265910.265918     Document Type: Article

References (47)

1. AMSTERDAM, J. 1988. Extending the Valiant learning model. In Proceeding of the 5th International Workshop on Machine Learning. pp. 364-375.
2. ANGLUIN, D. 1988. Queries and concept learning. Mach. Learn. 2, 4 (Apr.), 319-342.
3. ANGLUIN, D. 1992. Computational learning theory: Survey and selected bibliography. In Proceedings of the 24th Annual ACM Symposium on Theory of Computing (Victoria, B.C., Canada, May 4-6). ACM, New York, pp. 351-369.
4. ANGLUIN, D., AND LAIRD, P. 1988. Learning from noisy examples. Mach. Learn. 2, 4, 343-370.
5. ANGLUIN, D., AND SLONIM, D. K. 1994. Randomly fallible teachers: Learning monotone DNF with an incomplete membership oracle. Mach. Learn. 19, 7-126.
6. BACCHUS, F., GROVE, A., HALPERN, J. Y., AND KOLLER, D. 1993. Statistical foundations for default reasoning. In Proceedings of the International Joint Conference of Artificial Intelligence. 563-569.
7. BACCHUS, F., GROVE, A., HALPERN, J. Y., AND KOLLER, D. 1996. From statistical knowledge bases to degrees of beliefs. Artif. Int. 87, 1-2, 75-143.
8. BROOKS, R. A. 1991. Intelligence without representation. Artif. Int. 47, 139-159.
9. BSHOUTY, N. H. 1995. Exact learning via the monotone theory. Inf. Comput. 123, 1, 146-153.
10. CADOLI, M. 1995. Tractable Reasoning in Artificial Intelligence. In Lecture Notes in Artificial Intelligence, vol. 941. Springer-Verlag, New York.
11. DECHTER, R., AND PEARL, J. 1992. Structure identification in relational data. Artif. Int. 58, 237-270.
12. DOYLE, J., AND PATIL, R. 1991. Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services. Artif. Int. 48, 261-297.
13. FAGIN, R., HALPERN, J. Y., MOSES, Y., AND VARDI, M. Y. 1995. Reasoning about Knowledge. MIT Press, Cambridge, Mass.
14. FRAZIER, M., AND PITT. L. 1993. Learning from entailment: An application to propositional Horn sentences. In Proceedings of the International Conference on Machine Learning. Morgan-Kaufmann, San Mateo, Calif.
15. GREINER, R., GROVE, A., AND ROTH, D. 1996. Learning active classifiers. In Proceedings of the International Conference on Machine Learning.
16. HAUSSLER, D. 1987. Bias, version spaces and Valiant's learning framework. In Proceedings of the 4th International Workshop on Machine Learning. Univ. California, Irvine, Irvine, Calif.
17. HENRION, M. 1988. Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In Uncertainty in Artificial Intelligence.
18. JACKSON, J. 1994. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. In Proceedings of the IEEE Symposium on Foundation of Computer Science. IEEE, New York, pp. 42-53.
19. KAUTZ, H., KEARNS, M., AND SELMAN, B. 1993. Reasoning with characteristic models. In Proceedings of the National Conference on Artificial Intelligence, pp. 34-39.
20. KAUTZ, H., KEARNS, M., AND SELMAN, B. 1995. Horn approximations of empirical data. Artif. Int. 74, 129-145.
21. KEARNS, M. 1992. Oblivious pac learning of concepts hierarchies. In Proceedings of the National Conference on Artificial Intelligence, pp. 215-222.
22. KEARNS, M., AND LI, M. 1988. Learning in the presence of malicious errors. In Proceedings of the 20th Annual ACM Symposium on Theory of Computing (Chicago, Ill., May 2-4). ACM, New York, pp. 267-280.
23. KHARDON, R. 1996. Learning to take actions. In Proceedings of the National Conference on Artificial Intelligence, pp. 787-792.
24. KHARDON, R., AND ROTH, D. 1995. Learning to reason with a restricted view. In Workshop on Computational Learning Theory, pp. 301-310.
25. KHARDON, R., AND ROTH, D. 1996. Reasoning with models. Artif. int. 87, 1-2, 187-213.
26. KHARDON, R., AND ROTH, D. 1997. Default-reasoning with models. Artif. Int. J., to appear.
27. KIRSH, D. 1991. Foundations of AI: The big issues. Artif. Int. 47, 3-30.
28. LEVESQUE, H. J. 1984. Foundations of a functional approach to knowledge representation. Artif. Int. 23, 155-212.
29. LEVESQUE, H. 1986. Making believers out of computers. Artif. Int. 30, 81-108.
30. LEVESQUE, H. 1992. Is reasoning too hard? In Proceeding of the 3rd NEC Research Symposium.
31. LEVESQUE, H., AND BRACHMAN, R. 1985. A fundamental tradeoff in knowledge representation and reasoning. In Readings in Knowledge Representation, R. Brachman and H. Levesque, eds. Morgan-Kaufman, San Mateo, Calif.
32. LITTLESTONE, N. 1988. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Mach. Learn. 2, 285-318.
33. MCCARTHY, J. 1985. Programs with common sense. In Readings in Knowledge Representation, 1985, R. Brachman and H. Levesque, eds. Morgan-Kaufmann, San Mateo, Calif.
34. MOSES, Y., AND TENNENHOLTZ, M. 1996. Off-line reasoning for on-line efficiency: Knowledge bases. Artif. Int. 83, 2, 229-239.
35. NILSSON, N. J. 1986. Probabilistic logic. Artif. Int. 28, 71-87.
36. NILSSON, N. J. 1991. Logic and artificial intelligence. Artif. Int. 47, 31-56.
37. PAPADIMITRIOU, C. H. 1991. On selecting a satisfying truth assignment. In Proceedings of the 32nd Annual IEEE Symposium on Foundations of Computer Science. IEEE, New York.
38. ROTH, D. 1995. Learning to reason: The non-monotonic case. In Proceedings of the International Joint Conference of Artificial Intelligence, pp. 1178-1184.
39. ROTH, D. 1996. On the hardness of approximate reasoning. Artif. int. 82, 1-2 (Apr.), 273-302.
40. SELMAN, B. 1990. Tractable default reasoning. Ph.D. dissertation. Department of Computer Science, University of Toronto, Toronto, Ont., Canada.
41. SELMAN, B., AND KAUTZ, H. 1996. Knowledge compilation and theory approximation. J. ACM 43, 2 (Mar.), 193-224.
42. SHASTRI, L. 1993. A computational model of tractable reasoning - Taking inspiration from cognition. In Proceeding of the International Joint Conference of Artificial Intelligence (Aug.). pp. 202-207.
43. VALIANT, L. G. 1984. A theory of the learnable. Commun. ACM 27, 11 (Nov.), 1134-1142.
44. VALIANT, L. G. 1985. Learning disjunctions of conjunctions. In Proceedings of the International Joint Conference of Artificial Intelligence. Morgan-Kaufmann, San Mateo, Calif., pp. 560-566.
45. VALIANT, L. G. 1994. Circuits of the Mind (Nov.). Oxford University Press, Oxford, England.
46. VALIANT, L. G. 1994. Rationality. In Workshop on Computational Learning Theory (July). pp. 3-14.
47. VALIANT, L. G., AND VAZIRANI, V. V. 1986. NP is as easy as detecting unique solutions. Theoret. Comput. Sci. 47, 85-93.