A Non-Numeric Approach to Uncertain Reasoning

Abstract

This paper presents a non-numeric approach to uncertain reasoning by extending the incidence calculus. In parallel to the well known fuzzy, belief/plausibility, probability, and necessity/possibility measures, the corresponding classes of non-numeric functions are examined. A method of constructing non-numeric functions is discussed using the notion of compatibility relations. Non-numeric functions are used to interpret uncertain reasoning by providing possible-worlds semantics for both qualitative and quantitative methods.INDEX TERMS: Belief/plausibility, fuzzy measure, incidence calculus, interval structure, necessity/possibility, probability, non-numeric functions, rough sets, uncertain reasoning.

Two supervised learning approaches for name disambiguation in author citations

“…semantic word clustering on paper titles and journal titles [41]. A researcher usually has a research area or areas that do not change over a period of time, and his/her paper or submitted journal titles are closely related to his/her research topic.…”

Interval approaches for uncertain reasoning

“…By using the same argument, one can lift operations in a Boolean algebra or a lattice [19]. Such interval algebras may be used for reasoning with interval extension of classical logic [17], and interval incidence calculus [20]. [19].…”

“…To resolve some of these problems, various proposals have been suggested using intervals as truth values [2,3,[10][11][12]15,[17][18][19]. These studies have resulted in many interval based tools for uncertain reasoning, such as incidence calculus [3,20], interval structures [14], belief and plausibility functions [12], necessity and possibility functions [4], and interval fuzzy reasoning [19].…”

“…Let W denote a set of possible worlds. The power set 2 W , equipped with set-theoretic operations ∼, ∩, and ∪, is used as the set of truth values [3,17,20]. An evaluation function i associates a proposition in L(Φ) with a subset of W , and must satisfy the axioms:…”

A Novel Rough Set Model in Generalized Single Valued Neutrosophic Approximation Spaces and Its Application

“…The theory has been successfully applied to many fields, such as machine learning, knowledge acquisition, decision analysis, etc. To extend the application domain of rough set theory, more and more researchers have made some efforts toward the study of rough set models based on two different universes [29][30][31][32][33][34][35][36][37][38][39].…”