Cognitive Models of Plausible Reasoning- GMU Machine Learning and Inference Laboratory

cognitive models of plausible reasoning

(Michalski, Sklar)

The ability to reason plausibly, that is to derive useful conclusions from imperfect premises, is one of the most remarkable properties of the human mind, and a key to understanding intelligent behavior. In plausible reasoning, the premises may be incomplete, uncertain, imprecise or only partially relevant to the task. Yet, people are able to make useful conclusions from premises. The initial core theory of human plausible reasoning was developed by Collins and Michalski (1990--see MLI publications). The goals of this research are to develop a computational theory and models of plausible reasoning, to validate the theory by experiments involving the models and human subjects, and to apply it to developing a new approach to knowledge representation, filling gaps in databases, and dynamic recognition.

Selected References

Boehm-Davis, D., Dontas, K. and Michalski, R.S., "A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning," Reports of the Machine Learning and Inference Laboratory, MLI 90-5, School of Information Technology and Engineering, George Mason University, January 1990.

Collins, A. and Michalski, R.S., "The Logic of Plausible Reasoning: A Core Theory," Cognitive Science, Vol. 13, pp. 1-49, 1989.

Michalski, R.S., Dontas, K. and Boehm-Davis, D., "Plausible Reasoning: An Outline of Theory and Experiments," Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems, pp. 17-19, Charlotte, NC, October 1989.

Dontas, K., "APPLAUSE: An Implementation of the Collins-Michalski Theory of Plausible Reasoning," M.S. Thesis, Computer Science Department, University of Tennessee, Knoxville, TN, August 1988.

For more references, seeĀ publications section.

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