Eliminate the Vague? It might be argued that vagueness is an obstacle to clarity of meaning. ● But there does seem to be a loss of expressiveness when statements like,“Dan is balding”are eliminated from the language. This is what happens when natural language is translated into classic logic.The loss is not severe for accounting programs or computational mathematics programs,but will appear when the programming task turns to issues of queries and knowledge
Eliminate the Vague? ⚫ It might be argued that vagueness is an obstacle to clarity of meaning. ⚫ But there does seem to be a loss of expressiveness when statements like, “Dan is balding” are eliminated from the language. ⚫ This is what happens when natural language is translated into classic logic. The loss is not severe for accounting programs or computational mathematics programs, but will appear when the programming task turns to issues of queries and knowledge
What Is Lost...... 1.0 t妇ll(u=1.0) sharpedged membership degree of function for membership,u TALL 0.0 not tall (u =0.0) height 1.0 oontinuous defin itely a tall membersh ip person (u =0.95) degree of function for membership,p TALL really not very tall at all (u=0.30) 0.0 height
What Is Lost……
Could Be Significant excellent! You must be taller than this line to be considered TALL
Could Be Significant
Experts are vague To design an expert system a major task is to codify the expert's decision-making process. In a domain there may be precise,scientific tests and measurements that are used in a "fuzzy", intuitive manner to evaluate results,symptoms, relationships,causes,or remedies. While some of the decisions and calculations could be done using traditional logic,fuzzy systems afford a broader,richer field of data and manipulations than do more traditional methods
Experts are Vague ⚫ To design an expert system a major task is to codify the expert’s decision-making process. ⚫ In a domain there may be precise, scientific tests and measurements that are used in a “fuzzy” , intuitive manner to evaluate results, symptoms, relationships, causes, or remedies. ⚫ While some of the decisions and calculations could be done using traditional logic, fuzzy systems afford a broader, richer field of data and manipulations than do more traditional methods
Bivalence Boolean logic assumes that every element is either a member or a non-member of a given set (never both).This imposes an inherent restriction on the representation of imprecise concepts. ●For example at100°F a room is hot'and at 25°F it is“cold'. If the room temperature is 75 F,it is much more difficult to classify the temperature as "hot"or "cold". Boolean logic does not provide the means to identify an intermediate value
Bivalence ⚫ Boolean logic assumes that every element is either a member or a non-member of a given set (never both). This imposes an inherent restriction on the representation of imprecise concepts. ⚫ For example at 100°F a room is “hot” and at 25°F it is “cold”. ⚫ If the room temperature is 75°F, it is much more difficult to classify the temperature as “hot” or “cold”. ⚫ Boolean logic does not provide the means to identify an intermediate value