Vagueness is central to the flexibility and robustness of natural
language descriptions. Vague concepts are robust to the imprecision
of our perceptions, while still allowing us to convey useful, and
sometimes vital, information. The study of vagueness in Artificial
Intelligence (AI) is therefore motivated by the desire to
incorporate this robustness and flexibility into intelligent
computer systems. Such a goal, however, requires a formal model of
vague concepts that will allow us to quantify and manipulate the
uncertainty resulting from their use as a means of passing
information between autonomous agents.
This volume outlines a formal representation framework for
modelling and reasoning with vague concepts in Artificial
Intelligence. The new calculus has many applications, especially in
automated reasoning, learning, data analysis and information
fusion. This book gives a rigorous introduction to label semantics
theory, illustrated with many examples, and suggests clear
operational interpretations of the proposed measures. It also
provides a detailed description of how the theory can be applied in
data analysis and information fusion based on a range of benchmark
problems.
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