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Modifiable combining functions1

Published online by Cambridge University Press:  27 February 2009

Paul R. Cohen ,
Glenn Shafer  and
Prakash P. Shenoy
Paul R. Cohen
Affiliation:
Computer and Information Science, University of Massachusetts, Amherst, MA 01003 and School of Business, University of Kansas, Lawrence, KA 66045, U.S.A.
Glenn Shafer
Affiliation:
Computer and Information Science, University of Massachusetts, Amherst, MA 01003 and School of Business, University of Kansas, Lawrence, KA 66045, U.S.A.
Prakash P. Shenoy
Affiliation:
Computer and Information Science, University of Massachusetts, Amherst, MA 01003 and School of Business, University of Kansas, Lawrence, KA 66045, U.S.A.
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Abstract

Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 1 , February 1987 , pp. 47 - 57
Copyright
Copyright © Cambridge University Press 1987

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References

Bylander, T. and Mittal, S. 1986. CSRL: A language for classificatory problem solving and uncertainty handling. Al Magazine 7 (3), 66–77.Google Scholar
Chandrasakeran, B., Mittal, S., and Smith, J. W. 1982. Reasoning with uncertain knowledge: the MDX approach. Proceedings of the Congress of American Medical Informatics Association, San Francisco, pp. 335–339.Google Scholar
Cohen, P., Day, D., Delisio, J., Greenberg, M., Kjeldsen, R., Suthers, D., and Berman, P. 1987. Management of uncertainty in medicine. International Journal of Approximate Reasoning. 1(1). Forthcoming.CrossRefGoogle Scholar
Gruber, T. R. and Cohen, P. R. 1987. Design for acquisition: principles of knowledge system design to facilitate knowledge acquisition. International Journal of Man Machine Studies 26, 143–159.CrossRefGoogle Scholar
Patil, R.S., Szolovits, P. and Schwartz, W. B. 1981. Causal understanding of patient illness in medical diagnosis. Proceedings of 7th International Conference on Artificial Intelligence. British Columbia: William Kaufmann Publishers, pp. 893–899.Google Scholar
Pauker, S., Gorry, G., Kassirer, J. and Schwartz, W. 1976. Toward the simulation of clinical cognition: Taking a present illness by computer. American Journal of Medicine. 60, 981–995.CrossRefGoogle Scholar
Pearl, J. 1986. Fusion, propagation, and structuring in Bayesian networks. Artificial Intelligence 29, 24–288.CrossRefGoogle Scholar
Pople, H. 1977. The formation of composite hypotheses in diagnostic problem solving—an exercise in synthetic reasoning, Proceedings of the Fifth International Joint Conference on Artificial Intelligence. British Columbia: William Kaufmann. pp. 1030–1037.Google Scholar
Samuel, A. L. 1959. Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 3, 210–229.CrossRefGoogle Scholar
Shafer, G. 1981. Jeffrey's rule of conditioning. Philosophy of Science 48(3), 337–362.CrossRefGoogle Scholar
Shortliffe, E. 1976. Computer-Based Medical Consultations: MYCIN. New York: American Elsevier.Google Scholar
Shortliffe, E. H. and Buchanan, B. G. 1975. A model of inexact reasoning in medicine. Mathematical Biosciences 23, 351–379.CrossRefGoogle Scholar
Shafer, G. and Tversky, A. 1985 Languages and Designs for Probability Judgment, Cognitive Science 9, 309–339.Google Scholar
Shenoy, P. and Shafer, G. 1986. Propagating belief functions with local computations. IEEE Expert 1(3), 43–52.CrossRefGoogle Scholar
Trigoboff, M. 1978. IRIS: A framework for the construction of clinical consultation systems. Doctoral dissertation, Computer Science Dept. Rutgers University.Google Scholar
Weiss, Kulikowski and Safir, . 1977. A model-based consultation system for the long-term management of Glaucoma. Proceedings of the Fifth International Joint Conference on Artificial Intelligence. British Columbia: William Kaufmann. pp. 826–832.Google Scholar