Title: Drawing reasonable conclusions from information under similarity modelled contexts

Authors: Ronald R. Yager

Addresses: Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, USA

Abstract: We are interested in the process of making reasonable conclusions about the value of a variable. We indicate that reasonableness generally depends on the information we have about the variable as well as the context in which we shall use the assumed value. In order to include a wide range of imprecise and uncertain information, we use granular computing technologies such as fuzzy sets, Dempster-Shafer belief structures and probability theory to represent our knowledge and conclusions. While context is a very diverse idea, in order to provide some structure, we restrict ourselves to the special case where context can be modelled using a similarity relationship. Within this framework, we suggest a measure of the reasonableness of drawing conclusions from information in the context of a similarity relationship. We look at the properties of this measure and investigate its performance in a number of special cases.

Keywords: granular computing; context; similarity relationship; reasonable assumptions; drawing conclusions; conjecturing; uncertainty reduction; similarity modelling; variables; fuzzy sets; Dempster-Shafer belief structures; probability theory; reasonableness measures.

DOI: 10.1504/IJGCRSIS.2009.026726

International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2009 Vol.1 No.1, pp.81 - 104

Published online: 24 Jun 2009 *

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