nsically imprecise. A concept that plays a key role in perception-based decision analysis is that of precisiated natural language, PNL. In PDA, precisiated natural language is employed to define the goals, constraints, relations, and decision-relevant information. An important sublanguage of PNL is the language of fuzzy if-then rules, FRL. In this language, a perception of a function, f, is described by a collection of fuzzy if-then rules. Such a collection is referred to as the fuzzy graph of function f. Employment of PNL in decision analysis adds an important capability--a capability to operate on perception-based information. The capability has the effect of substantially enhancing the ability of DA to deal with real-world problems. More fundamentally, the high expressive power of PNL opens the door to a redefinition of such basic concepts as optimality and causality. Perception-based decision analysis represents a significant change in direction in the evolution of decision analysis. As we move farther into the age of machine intelligence and automation of reasoning, the need for a shift from computing with numbers to computing with words, and from manipulation of measurements to manipulation of perceptions, will cease to be a matter of debate.
From Computing with Numbers to Computing with Words to Computation with Perceptions--A Paradigm Shift:
The theory put forth in this research is focused on the development of what is referred to as the computational theory of perceptions (CTP)--a theory that comprises a conceptual framework and a methodology for computing and reasoning with perceptions. The base for CTP is the methodology of computing with words (CW). In CW, the objects of computation are words and propositions drawn from a natural language. The point of departure in the computational theory of perceptions is the assumption that perceptions are described as propositions in a natural language. Furthermore, computing and reasoning with perceptions is reduced to computing and reasoning with words.
To be able to compute with perceptions it is necessary to have a means of representing their meaning in a way that lends itself to computation. Conventional approaches to meaning representation cannot serve this purpose because the intrinsic imprecision of perceptions puts them well beyond the expressive power of predicate logic and related systems.
In the computational theory of perceptions, representation of meaning is a preliminary to reasoning with perceptions--a process that starts with a collection of perceptions that constitute the initial data set (IDS) and terminates in a proposition or a collection of propositions that play the role of an answer to a query, that is, the terminal data set (TDS).
The principal aim of the computational theory of perceptions is the development of an automated capability to reason with perception-based information. Existing theories do not have this capability and rely instead on conversion of perceptions into measurements--a process that in many cases is infeasible, unrealistic, or counterproductive. In this perspective, addition of the machinery of the computational theory of perceptions to existing theories may eventually lead to theories that have a superior capability to deal with real-world problems and make it possible to conceive and design systems with a much higher MIQ (Machine IQ) than those we have today.