GERTIS: A dempster-shafer approach to diagnosing hierarchical hypotheses

Communications of the ACM

Volumn 32, Issue 5, 1989, Pages 573-585

  Yen, John ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE REASONING; DEMPSTER-SHAFER THEORY; GERTIS; HIERARCHICAL HYPOTHESES; REASONING MODELS; RULE BASED SYSTEMS;

ARTIFICIAL INTELLIGENCE;

EID: 0024656125     PISSN: 00010782     EISSN: 15577317     Source Type: Journal    
DOI: 10.1145/63485.63488     Document Type: Article

References (32)

1. Adlassnig, K.-P. Present state of the medical expert system CADIAG-2. Methods of Information in Medicine 24, 1 (1985), 13-20.
2. Adlassnig, K.-P. CADIAG: Approaches to computer-assisted medical diagnosis. Comput. Biol. Med. 15, 5 (1985), 315-335.
3. Aikins, J.S. Prototypes and production rules: A knowledge representation for computer consultations. Tech. Rep. STAN-CS-80-814, Department of Computer Science, Stanford University, 1980.
4. Baldwin, J.F. Support logic programming. In A. Jones, A. Kaufman, and H.-J. Zimmermann, Eds., Fuzzy Sets Theory and Applications. D. Reidel Publishing Company, 1986, pp. 133-170.
5. Chatalic, P., Dubois, D., and Prade. H. An approach to approximate reasoning based on the Dempster rule of combination. Int. J. Exp. Syst. 1, 1 (1987), 67-85.
6. Clancey, W. The epistemology of a rule-based expert system: A framework for explanation. Art. Intell. 20, 3 (May 1983), 215-251.
7. Cohen, P., and Grinberg, M. A theory of heuristic reasoning about uncertainty. AI Mag. 4, 2 (Summer 1983), 17-24.
8. Dempster, A.P. Upper and lower probabilities induced by a multi-valued mapping. Annals Math. Stat. 38, (1967), 325-339.
9. Duda, R.O., Hart, P.E., and Nilsson, N.J. Subjective Bayesian methods for rule-based inference systems. In Proceedings of the 1976 National Computer Conference. (AFIPS, 1976) pp. 1075-1082.
10. Ginsberg, M.L. Non-monotonic reasoning using Dempster'r rule. In Proceedings of the 4th National Conference on Artificial Intelligence, AAAI (Austin, Texas, Aug. 1984), pp. 126-129.
11. Gordon, J., and Shortliffe, E.H. A method for managing evidential reasoning in a hierarchical hypothesis space. Art. Intell. 26, 3 (Jul. 1985), 323-357.
12. Grosof, B.N. Evidential confirmation as transformed probability. In Proceedings of the AAAI/RCA Workshop on Uncertainty and Probability in Artificial Intelligence (Los Angeles, Aug. 1985), pp. 185-192.
13. Heckerman, D. A probabilistic interpretation for MYCIN'N certainty factors. In Proceedings of the AAAI/RCA Workshop on Uncertainty and Probability in Artificial Intelligence (Los Angeles, Aug. 1985), pp. 9-20.
14. Heckerman, D. A rational measure of confirmation. Tech. Rep. MEMO KSL-86-16, Department of Medicine and Computer Science, Stanford University School of Medicine, February, 1986.
15. Ishizuka, M., Fu, K.S., and Yao, J.T.P. Inference procedures and uncertainty for the problem-reduction method. Inf. Sci. 28 (1982), 179-206.
16. Konolige, K. Bayesian methods for updating probabilities (Appendix D) in A Computer-Based Consultant for Mineral Exploration. Tech. Rep. Final Report of Project 6415, SRI International, Menlo Park, California, 1982.
17. Lu, S.Y., and Stephanou, H.E. A set-theoretic framework for the processing of uncertain knowledge. In Proceedings of the 4th National Conference on Artificial Intelligence (Austin, Texas, 1984) pp. 216-221.
18. Minsky, M. A framework for representing knowledge. Ir. P. Winston, Ed., The Psychology of Computer Vision. McGraw-Hill, New York, 1975, 211-278.
19. Neches, R., Swartout, W., and Moore, J. Enhanced maintenance and explanation of expert systems through explicit models of their development. IEEE Trans. Softw. Eng. SE-11, 11 (Nov. 1985), 1337-1351.
20. Pearl, J. On evidential reasoning in a hierarchy of hypothesis. Art. Intell 28, 1 (Feb. 1986), 9-16.
21. Pearl, J. Fusion, propagation and structuring in belief networks. Art. Intell 29, 3 (Sept. 1986), 241-288.
22. Reboh, R. Extracting useful advice from conflicting expertise. In Proceedings of the IJCAI-83. (Karlsruhe, W. Germany, Aug. 1983), pp. 145-150.
23. Ruspini, E.H. Approximate inference and interval probabilities. In Uncertainty in Knowledge-Based Systems. B. Bouchon and R.R. Yager. Eds. Springer-Verlag, 1987.
24. Shafer, G. Mathematical Theory of Evidence. Princeton University Press, Princeton, N.J., 1976.
25. Shafer, G., and Logan, R. Implementing Dempster'r rule for hierarchical evidence. Art. Intell. 33, 3 (Nov. 1987), 271-298.
26. Shenoy, P.P., and Shafer, G. Propagating belief functions using local computations. IEEE Expert 1, 3 (1986), 43-52.
27. Shortliffe, E.H., and Buchanan. B.G. A model of inexact reasoning in medicine. Math. Biosci. 23 (1975), 351-379.
28. Strat, T.M. The generation of explanations within evidential reasoning systems. In Proceedings of the 10th International Joint Conference on Artificial Intelligence. IJCAII, Milan, Italy, Aug. 1987, pp. 1097-1104.
29. Swartout, W. XPLAIN: A system for creating and explaining expert consulting systems, Art. Intell 21, 3 (Sept. 1983), 285-325.
30. Yen, J. A reasoning model based on an extended Dempster-Shafer theory, In Proceedings of the 5th National Conference on Artificial Intelligence (Philadelphia. Pa, 1986), pp. 125-131.
31. Yen, J. Evidential Reasoning in Expert Systems. Ph.D. dissertation. Department of Electrical Engineering and Computer Science, Computer Science Division, University of California. Berkeley, 1986.
32. Zadeh, L.A. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Mag. 7, 2 (1986), 85-90.