Nowadays, knowledge-based system has to be able to model and treat imperfect knowledge. Among the... more Nowadays, knowledge-based system has to be able to model and treat imperfect knowledge. Among the knowledge imperfection, we cite imprecision. Imprecise information are generally represented in a quantitative way using fuzzy logic or in a qualitative way using symbolic multi-valued logic. As far as we knew, no work has considered both fuzzy and symbolic multi-valued knowledge simultaneously in the same knowledge-based system. However, the user is often in need of both data types to insure a relevant decision-making. In order to improve the decision-making process performance, we propose in this paper an approach, that is able to standardize input knowledge. In fact, we propose a fuzzy-to-symbolic conversion of inputs by projecting them over the abscissa axis. We apply the proposed conversion module in symbolic inference systems. Thus, a symbolic approximate reasoning can be executed. The conversion process involves the expert by asking him to express its tolerance threshold toward handled fuzzy knowledge. Thus, a minimum of fuzzy information loss will be insured according to the expert preferences and the reasoning context. Our proposal is also useful even when the rule conclusion is originally fuzzy. In that case, a symbolic-to fuzzy conversion of the inference result is required to make the inference result more intelligible for the user and to maintain the transparency of the fuzzy-to-symbolic conversion. A numerical study is provided to illustrate the potential applications of the proposed methodology.
Nowadays, knowledge-based system has to be able to model and treat imperfect knowledge. Among the... more Nowadays, knowledge-based system has to be able to model and treat imperfect knowledge. Among the knowledge imperfection, we cite imprecision. Imprecise information are generally represented in a quantitative way using fuzzy logic or in a qualitative way using symbolic multi-valued logic. As far as we knew, no work has considered both fuzzy and symbolic multi-valued knowledge simultaneously in the same knowledge-based system. However, the user is often in need of both data types to insure a relevant decision-making. In order to improve the decision-making process performance, we propose in this paper an approach, that is able to standardize input knowledge. In fact, we propose a fuzzy-to-symbolic conversion of inputs by projecting them over the abscissa axis. We apply the proposed conversion module in symbolic inference systems. Thus, a symbolic approximate reasoning can be executed. The conversion process involves the expert by asking him to express its tolerance threshold toward handled fuzzy knowledge. Thus, a minimum of fuzzy information loss will be insured according to the expert preferences and the reasoning context. Our proposal is also useful even when the rule conclusion is originally fuzzy. In that case, a symbolic-to fuzzy conversion of the inference result is required to make the inference result more intelligible for the user and to maintain the transparency of the fuzzy-to-symbolic conversion. A numerical study is provided to illustrate the potential applications of the proposed methodology.
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Papers by Soumaya Moussa