Abstract
The paper describes knowledge acquisition under uncertainty using rough set theory, a concept introduced by Z. Pawlak in 1981. A collection of rules is acquired, on the basis of information stored in a data base-like system, called an information system. Uncertainty implies inconsistencies, which are taken into account, so that the produced rules are categorized into certain and possible with the help of rough set theory. The approach presented belongs to the class of methods of learning from examples. The taxonomy of all possible expert classifications, based on rough set theory, is also established. It is shown that some classifications are theoretically (and, therefore, in practice) forbidden.
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Arciszewski, T. and Ziarko, W., Adaptive expert system for preliminary engineering design, Proc. 6th Internat. Workshop on Expert Systems and their Applications, Avignon, France, Vol. 1, pp. 696–712 (1986).
Bobrow, D. G., Mittal, S., and Stefik, M. J., Expert systems: perils and promise, Com. ACM 29, 880–894 (1986).
Cheeseman, P., Induction of models under uncertainty, Proc. ACM SIGART Internat. Symposium on Methodologies for Intelligent Systems, Knoxville, Tennessee, pp. 130–144 (1986).
Cohen, P. R. and Feigenbaum, E. A., The Handbook of Artificial Intelligence, Vol. 3, W. Kaufmann, Inc., (1982).
Cohen, P. R. and Grinberg, M. R., A framework for heuristic reasoning about uncertainty, Proc. 8th IJCAI, Karlsruhe, W. Germany, pp. 355–357 (1983).
Dubois, D. and Prade, H., Twofold fuzzy sets and rough sets, Fuzzy Sets and Systems 17 (1985).
Farinas del Cerro, L. and Prade, H., Rough sets, twofold fuzzy sets and modal logic, to appear in Topics in the Mathematics of Fuzzy Systems (ed. A. Di Nola, A.G.S. Ventre), Verlag Tuev, Rheinland, Cologne.
Fibak, J., Slowinski, K., and Slowinski, R., The application of rough set theory to the verification of indications for treatment of duodenal ulcer by HSV, Proc. 6th Internat. Workshop on Expert Systems and their Applications, Avignon, France, Vol. 1, pp. 587–599 (1986).
Grzymala-Busse, J. W., On the reduction of knowledge representation systems, Proc. 6th Internat. Workshop on Expert Systems and their Applications, Avignon, France, Vol. 1, pp. 463–478 (1986).
Mamdani, A., Efstathiou, J., and Pang, D., Inference under uncertainty, Expert Systems 85. Proc. Fifth Tech. Conf. British Computer Soc., Specialist Group on Expert Systems, pp. 181–194 (1985).
McCarthy, J., Circumscription—a form of non-monotonic reasoning, AI 13, 27–39 (1980).
McDermott, D. and Doyle, J., Non-monotonic logic I, AI 13, 41–72 (1980).
Michalski, R. S. and Chilausky, R. L., Learning by being told and learning from examples: An experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis, Inter. J. Poly. Anal. Infor. Sys. 4, 125–161 (1980).
Mrozek, A., Information systems and control algorithms, Bull. Polish Acad. Sci., Technical Sci. 33, 195–204 (1985).
Mrozek, A., Rough sets and some aspects of expert systems realization, Proc. 7th Internat. Workshop on Expert Systems and their Applications, Avignon, France, pp. 597–611 (1987).
Pawlak, Z., Rough sets. Basic notions, Institute Comp. Sci. Polish Acad. Sci. Rep. No. 431, Warsaw (1981).
Pawlak, Z., Classification of objects by means of attributes, Institute Comp. Sci. Polish Acad. Sci. Rep. No. 429, Warsaw, (1981).
Pawlak, Z., Rough sets, Int. J. Information Computer Sci. 11, 341–356 (1982).
Pawlak, Z., Rough classification, Int. J. Man-Machine Studies 20, 469–483 (1983).
Pawlak, Z., Rough sets and fuzzy sets, Fuzzy Sets and Systems 17, 99–102 (1985).
Quinlan, J. R., Consistency and plausible reasoning, Proc. 8th IJCAI Karlsruhe, W. Germany, pp. 137–144 (1983).
Reichgelt, H. and Van Harmelen, F., Relevant criteria for choosing an inference engine in expert systems, Expert Systems 85. Proc. Fifth Technical Conf. British Computer Soc., Specialist Group on Expert Systems, pp. 21–30 (1985).
Shafer, G., Mathematical Theory of Evidence, Princeton University Press (1976).
Tanaka, K., Resume on dealing with uncertainty/ambiguity in conjunction with knowledge engineering, in Fuzzy Set and Possibility Theory. Recent Developments (ed. R. R. Yager), Pergamon Press, pp. 38–48 (1982).
Wiederhold, G. C. M., Walker, M., Blum, R., and Downs, S., Acquisition of knowledge from data, Proc. ACM SIGART Internat. Symp. on Methodologies for Intelligent Systems, Knoxville, Tennessee, pp. 78–84 (1986).
Wong, S. K. M. and Ziarko, W., INFER—an adaptive decision support system based on the probabilistic approximate classifications, Proc. 6th Internat. Workshop on Expert Systems and their Applications, Avignon, France, Vol. 1, pp. 713–726 (1986).
Yager, R. R., Approximate reasoning as a basis for rule based expert systems, IEEE Trans. on Systems, Man and Cybernetic 14, 636–643 (1984).
Zadeh, L. A., Fuzzy sets, Inf. and Control 8, 338–353 (1965).
Zadeh, L. A., The rule of fuzzy logic in the management of uncertainty in expert systems, Fuzzy Sets and Systems 11, 119–227 (1983).
Zadeh, L. A., A computational theory of dispositions, Proc. 1984 Int. Conf. of the Assoc. for Computational Linguistics.
Zadeh, L. A., A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination, Berkeley Cognitive Science Report No. 33, (1985); The AI Magazine 7, 85–90 (1986).
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Grzymala-Busse, J.W. Knowledge acquisition under uncertainty — a rough set approach. J Intell Robot Syst 1, 3–16 (1988). https://doi.org/10.1007/BF00437317
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DOI: https://doi.org/10.1007/BF00437317