Representation of qualitative user preference by quantitative belief functions

Many intelligent systems employ numeric degrees of belief supplied by the users to make decisions. However, the users may have difficulties in expressing their belief in terms of numeric values. The authors present a method for generating belief functions from symbolic information such as the qualit...

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Published in:IEEE transactions on knowledge and data engineering 1994-02, Vol.6 (1), p.72-78
Main Authors: Wong, S.K.M., Lingras, P.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Bayesian methods
Computer science
Computer science
control theory
systems
Decision support systems
Exact sciences and technology
Humans
Intelligent systems
Learning and adaptive systems
Probability
Quality management
Statistics
Uncertainty
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description Many intelligent systems employ numeric degrees of belief supplied by the users to make decisions. However, the users may have difficulties in expressing their belief in terms of numeric values. The authors present a method for generating belief functions from symbolic information such as the qualitative preference relationships. The method of generating belief functions provides a practical interface between the users and a decision support system. It can be argued that the ability to generate numeric judgments with nonnumeric inputs is essential in the development of approximate reasoning systems. The proposed method can provide an important component for these systems by transforming qualitative information into quantitative information.< >
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subjects Applied sciences
Artificial intelligence
Bayesian methods
Computer science
Computer science
control theory
systems
Decision support systems
Exact sciences and technology
Humans
Intelligent systems
Learning and adaptive systems
Probability
Quality management
Statistics
Uncertainty
title Representation of qualitative user preference by quantitative belief functions
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