Results 1 
5 of
5
The Paradoxical Success of Fuzzy Logic
 IEEE Expert
, 1993
"... Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to twovalued logic. Moreover, there are few if any ..."
Abstract

Cited by 69 (1 self)
 Add to MetaCart
Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to twovalued logic. Moreover, there are few if any published reports of expert systems in realworld use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledgebased systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledgebased systems. 1
Fuzzyshell: A largescale expert system shell using fuzzy logic for uncertainty reasoning
 IEEE Trans. Fuzzy Syst
, 1998
"... Abstract — There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. However, unfortunately, much of what has been proposed can only be applied to smallscale expert systems; that is, when the number of rules is in the dozens as opposed t ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
Abstract — There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. However, unfortunately, much of what has been proposed can only be applied to smallscale expert systems; that is, when the number of rules is in the dozens as opposed to in the hundreds. The more traditional (nonfuzzy) expert systems are able to cope with large numbers of rules by using Rete networks for maintaining matches of all the rules and all the facts. (A Rete network obviates the need to match the rules with the facts on every cycle of the inference engine.) In this paper, we present a more general Rete network that is particularly suitable for reasoning with fuzzy logic. The generalized Rete network consists of a cascade of three networks: the pattern network, the join network, and the evidence aggregation network. The first two layers are modified versions of similar layers for the traditional Rete networks and the last, the aggregation layer, is a new concept that allows fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules. Index Terms—Expert system, fuzzy logic, Rete network. I.
Design of a largescale expert system using fuzzy logic for uncertaintyreasoning
, 1994
"... There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. But, unfortunately, much of what has been proposed can only be applied tosmallscale expert systems, that is when the number of rules is in the dozens as opposed to in the hundreds ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. But, unfortunately, much of what has been proposed can only be applied tosmallscale expert systems, that is when the number of rules is in the dozens as opposed to in the hundreds. Rete networks have been used in the more traditional expert systems to ameliorate the computational burden that would be associated with matching all the rules with all the facts on each cycle of the inference engine. In this paper, we present a more general Rete network that is particularly suitable for reasoning with fuzzy logic. In our new class of Rete networks, before anyfactsbecome available, there are the fuzzy membership functions associated with the di erent terms in the ruleantecedents. Upon the assertion of a fact into the working memory, the pattern matcher \pushes " the fact into the appropriate branches of the network and calculates via a supmin operation the degree of match between the fact and the rule term. This degreeofmatch number is then propagated down the rest of the network in keeping with the rules of the fuzzy logic employed. 1
Partial Truth is not Uncertaintv d Fuzzy Logic versus Possibilistic Logic
"... and cast serious doubts on the reasons for its success, arguing that “fuzzy logic collapses mathematically to twovalued logic. ” We completely disagree, and we especially object to two points: (1) Elkan’s proof uses too strong a notion of logical equivalence. The particular equivalence he considers ..."
Abstract
 Add to MetaCart
and cast serious doubts on the reasons for its success, arguing that “fuzzy logic collapses mathematically to twovalued logic. ” We completely disagree, and we especially object to two points: (1) Elkan’s proof uses too strong a notion of logical equivalence. The particular equivalence he considers, while valid in Boolean algebra, has nothing to do with fuzzy logic. (2) Elkan claims that De Morgan’s algebra “allows very little reasoning about collections of fuzzy assertions, ” although he correctly states that when logical equivalence is restricted to De
E&an’s Reply The Paradoxical Controversy over Fuzzy Logic
"... The responses to my article provide an that I have is whether the distinction is re knowledge becomes implicit background exceptionally wide range of perspectives ally well defined. On the one hand, there knowledge that must be used tacitly in tunon the current state of research on fuzzy may be mul ..."
Abstract
 Add to MetaCart
The responses to my article provide an that I have is whether the distinction is re knowledge becomes implicit background exceptionally wide range of perspectives ally well defined. On the one hand, there knowledge that must be used tacitly in tunon the current state of research on fuzzy may be multiple types of imprecision and ing the allowed interactions between the logic and its applications. Overall, I find vagueness. Is the domainindependent im items of explicit shallow knowledge. To that with most commentators I agree more precision involved in “around 1.80m ” the quote Garcia, “The dogma of generality than I disagree. I shall try here to steer a same as the humanspecific imprecision versus efficiency strikes again, and knowlmiddle course between simply repeating involved in “tall”? On the other hand, it edge engineering and machine learning are points of agreement and narrowly counter may be possible to model some types of not exempted.” ing points of disagreement. imprecision probabilistically. For example, the degree of truth of the assertion “ 1.SOm Fuzzy logic in expert systems. Only three The foundations of fuzzy logic. Some is tall ” might be modeled as the probability of the responses give references in an atcommentators take a more extreme posi that an individual with height 1.80m would tempt to dispute the claim that there are tion than I do concerning the coherence of be labeled as tall given incomplete knowl very few deployed expert systems that acfuzzy logic. I do not agree with Attikiouzel edge, that is, given no other information on tually use fuzzy logic as their principal