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Measuring information in possibilistic logic
Author(s) -
Yager Ronald R.
Publication year - 1999
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199905)14:5<475::aid-int2>3.0.co;2-g
Subject(s) - proposition , measure (data warehouse) , propositional calculus , computer science , artificial intelligence , non monotonic logic , possibility theory , description logic , mathematics , theoretical computer science , fuzzy logic , data mining , epistemology , fuzzy set , programming language , philosophy
We provide an overview of the theory of approximate reasoning and discuss the measurement of information in this reasoning system using specificity. It is then shown how to represent the binary propositional logic in the framework of approximate reasoning. Using the measure of specificity we show how to measure the information contained in the propositions of binary logic. Our measure essentially measures the proportion of possibilities eliminated by the proposition. Next we turn to the possibilistic logic and show how to represent this within the framework of approximate reasoning. We again, using specificity, provide for a measure of information of propositions in this logic. Finally we turn to the issue of quantified statements and show how to represent general quantified statements involving predicates within these two logics. ©1999 John Wiley & Sons, Inc.