Changing the Rules: A Comprehensive Approach to Theory Refinement

Proceedings of the 8th National Conference on Artificial Intelligence, AAAI 1990

Volumn , Issue , 1990, Pages 815-820

  Ourston, Dirk ^a   Mooney, Raymond J ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MOLECULAR BIOLOGY;

EXPERT SYSTEM DEVELOPMENT; INDUCTIVE SYSTEM; MULTIPLE FAULTS; SYSTEM LIFECYCLE;

EXPERT SYSTEMS;

EID: 84985051834     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. K. M. Ali. Augmenting domain theory for explanation-based generalization. In Proceedings of the Sixth International Conference on Machine Learning, pages 40-42, Ithaca, NY, June 1989.
2. F. Bergadano and A. Giordana. A knowledge intensive approach to concept induction. In Proceedings of the Fifth International Conference on Machine Learning, pages 305-317, Ann Arbor, MI, June 1988.
3. E. Charniak and D. McDermott. Introduction to AI. Addison-Wesley, Reading, MA, 1985.
4. A. P. Danyluk. Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings of the Sixth International Conference on Machine Learning, pages 34-36, Ithaca, NY, June 1989.
5. N. S. Flann and T. G. Dietterich. A study of explanation-based methods for inductive learning. Machine Learning, 4 (2):187-226, 1989.
6. A. Ginsberg, S. M. Weiss, and P. Politakis. Automatic knowledge based refinement for classification systems. Artificial Intelligence, 35:197-226, 1988.
7. A. Ginsberg. Theory revision via prior operationalization. In Proceedings of National Conference on Artificial Intelligence, pages 590-595, St. Paul, MN, August 1988.
8. H. Hirsh. Incremental Version-Space Merging: A General Framework for Concept Learning. PhD thesis, Stanford University, Palo Alto, CA, June 1989.
9. D. S. Johnson. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences, 9:256-278, 1974.
10. R. J. Mooney and D. Ourston. Induction over the unexplained: Integrated learning of concepts with both explainable and conventional aspects. In Proceedings of the Sixth International Conference on Machine Learning, pages 5-7, Ithaca, NY, June 1989.
11. H. T. Ng and R. J. Mooney. Abductive explanations for text understanding: Some problems and solutions. Technical Report AI89-116, Artificial Intelligence Laboratory, University of Texas, Austin, TX, August 1989.
12. M. J. Pazzani. Detecting and correcting errors of omission after explanation-based learning. In Proceedings of the Eleventh International Joint conference on Artificial intelligence, pages 713-718, Detroit, MI, Aug. 1989.
13. J. R. Quinlan. Induction of decision trees. Machine Learning, 1 (1):81-106, 1986.
14. S. A. Rajamoney and G. F. DeJong. Active explanation reduction: An approach to the multiple explanations problem. In Proceedings of the Fifth International Conference on Machine Learning, pages 242-255, Ann Arbor, MI, June 1988.
15. J. W. Shavlik and G. G. Towell. Combining explanation-based learning and artificial neural networks. In Proceedings of the Sixth International Conference on Machine Learning, pages 90-92, Ithaca, NY, June 1989.
16. M. E. Stickel. A prolog-like inference system for computing minimum-cost abductive explanations in natural-language interpretation. Technical Report Technical Note 451, SRI International, Menlo Park, CA, September 1988.
17. G. G. Towell, J. W. Shavlik, and Michiel O. Noordewier. Refinement of approximate domain theories by knowledge-based artificial neural networks. In Proceedings of National Conference on Artificial Intelligence, Boston, MA, July 1990.
18. D. C. Wilkins. Knowlege base refinement using apprenticeship learning techniques. In Proceedings of National Conference on Artificial Intelligence, pages 646-651, St. Paul, MN, August 1988.
19. P. H. Winston, T. O. Binford, B. Katz, and M. Lowry. Learning physical descriptions from functional definitions, examples, and precedents. In Proceedings of National Conference on Artificial Intelligence, pages 433-439, Washington, D. C., Aug. 1983.