COMPUTING WITH WORDS AND PERCEPTIONS (CWP) â€“ A SHIFT IN DIRECTION IN COMPUTING AND DECISION ANALYSIS
Keywords:fuzzy, CWP (computing with words), PNL (precisiated natural language), probability theory.
In computing with words and perceptions, or CWP for short, the objects of computation are words, propositions and perceptions described in a natural language. In science, there is a deep-seated tradition of striving for progression from perceptions to measurements, and from the use of words to the use of numbers.
Reflecting the bounded ability of sensory organs and, ultimately, the brain, to resolve detail, perceptions are intrinsically imprecise. Perceptions are f-granular in the sense that (a) the perceived values of attributes are fuzzy; and (b) the perceived values of attributes are granular, with a granule being a clump of values drawn together by indistinguishability, similarity, proximity or functionality.
F-granularity of perceptions is the reason why in the enormous literature on perceptions one cannot find a theory in which perceptions are objects of computation, as they are in CWP.
PNL (precisiated natural language) associates with a natural language, NL, a precisiation language, GCL (Generalized Constraint Language), which consists of generalized constraints and their combinations and qualifications.
The principal function of PNL is to serve as a system for computation and reasoning with perceptions. The need for redefinition arises because standard bivalent â€“ classic-based definitions may lead to counterintuitive conclusions.
Computing with words and perceptions provides a basis for an important generalization of probability theory, namely, perception-based probability theory (PTp).
The importance of CWP derives from the fact that it opens the door to adding to any measurement-based theory