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February 1992 (Vol. 7, No. 1)   pp. 28-35
Large Interactions of Compiled and Causal Reasoning in Diagnosis

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.120685
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Abstract
A medical diagnosis system that dynamically integrates two types of problem-solving agents to solve large problems is presented. It integrates a deep/model-based/fundamental type of system with a shallow/compiled/empirical type system. A functional-representation model serves as a deep reasoner, giving causal credence to diagnostic conclusions and finding interactions between partial conclusions that greatly reduce searches.
References
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[13] J. Sticklen and B. Chandrasekaran, "Integrating Classification-Based Compiled-Level Reasoning with Function-Based Deep-Level Reasoning,"Applied Artificial Intelligence, Vol. 3, No. 2, 1989.
[14] P. Koton, "Integrating Causal and Case-Based Reasoning for Clinical Problem Solving,"Proc. AAAI Spring Symp. Series, AI in Medicine, 1988, pp. 53-54.
[15] W.F. Punch III and B. Chandrasekaran, "An Investigation of the Roles of Problem-Solving Methods in Diagnosis,"Proc. Second-Generation Expert Systems Conf.(Avignon '90), 1990, pp. 25-36.
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Additional Information

Citation:  William F. Punch III, "Large Interactions of Compiled and Causal Reasoning in Diagnosis," IEEE Expert: Intelligent Systems and Their Applications, vol. 07,  no. 1,  pp. 28-35,  Feb.,  1992

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