Theoretical review reveals mathematical foundations for managing vagueness in computational systems, highlighting broad utility across artificial intelligence and decision analysis.
Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
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Pawlak et al. (1995) studied this question.
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