Concepts and fuzzy logic

Artificial Intelligence Review - AIR, 2004

Entemann (2002) defends fuzzy logic by pointing to what he calls “misconceptions” concerning fuzzy logic. However, some of these ‘;misconceptions’ are in fact truths, and it is Entemann who has the misconceptions. The present article points to mistakes made by Entemann in three different areas. It closes with a discussion of what sort of general considerations it would take to motivate fuzzy logic.

Entemann 2002) defends fuzzy logic by pointing to what he calls 'misconceptions' concerning fuzzy logic. However, some of these 'misconceptions' are in fact truths, and it is Entemann who has the misconceptions. The present article points to mistakes made by Entemann in three different areas. It closes with a discussion of what sort of general considerations it would take to motivate fuzzy logic.

In this chapter, I am concerned with a particular kind of concept. The term “concept” can be used for a wide variety of mental and cultural entities. We can talk about the concept of art, the concept of a mathematical function like addition, the concept of democracy or the concept of a space-time continuum. In fact it can be argued that the history all of human intellectual endeavour, outside of the arts, is the history of the development of concepts.

Allied Journals, 2015

The problems of uncertainty, imprecision and vagueness have been discussed for many years. These problems have been major topics in philosophical arenas with series of debate, in particular, about the nature of vagueness and the ability of traditional Boolean logic to cope with concepts and perceptions that are imprecise or vague. These sometimes make experts to rely on common sense when they solve problems. Interestingly, a lot of experts such as scientists, philosophers, psychologists, great mathematicians and others, researched vehemently on fuzzy logic, putting each other under constant scrutiny in response to their individual propositions coupled with fuzzy logic not until Zadeh Lotfi, eventually conceived fuzzy logic in 1971. Fuzzy logic is not logic that is fuzzy, but logic that is used to describe fuzziness. Fuzzy logic is the theory of fuzzy sets, sets that calibrate vagueness. Fuzzy logic is based on the idea that all things admit of degrees. Temperature, height, speed, distance, beauty, handsome, ugly, walk, stand all comes on a sliding scale. My car is running really hot. Elvis is a very tall guy. Fuzzy logic reflects how people think. It attempts to model our sense of words, our decision making and our common sense. As a result, it is leading to new, more human, intelligent systems.

Soft Computing, 2007

Inspired by human's remarkable capability to perform a wide variety of physical and mental tasks without any measurements and computations and dissatisfied with classical logic as a tool for modeling human reasoning in an imprecise environment, Lotfi A. Zadeh developed the theory and foundation of fuzzy logic with his 1965 paper "Fuzzy sets" (Zadeh in Inf Control 8:378-53, 1965) and extended his work with his 2005 paper "Toward a generalized theory of uncertainty (GTU)-an outline" (Zadeh in Inf Control, 2005). Fuzzy logic has at least two main sources over the past century. The first of these sources was initiated by Peirce in the form what he called a logic of vagueness in 1900s, and the second source is Lotfi's A. Zadeh work, fuzzy sets and fuzzy Logic in the 1960s and 1970s. Keywords Nature of mind • Zadeh • Fuzzy sets • Logic • Vagueness 1 Introduction Human have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. In traditional sense, computation means manipulation of numbers, whereas human uses words M. Nikravesh (B) BISC Program,

Studies in Fuzziness and Soft Computing, 2009

In spite of its successes as a tool in the field of engineering, fuzzy set theory has yet to achieve the universal footing that probability theory has across the various fields of mathematics, technology, philosophy and psychology. This paper sets out points of critique brought up regarding the fuzzy approach and seeks to analyze them, focusing on the question of whether anything that can be done about these matters. Do these criticisms have any practical relevance or any relevance with respect to the intended fields of usage. Do they or do they not diminish fuzzy logic's suitability as a theory of vagueness?

Studium Philosophicum , 2024

Fuzzy Logic is nowadays a very popular logic methodology. Different kinds of applications in cybernetics, in software programming and its growing use in medicine seems to make Fuzzy Logic a very important calculus in science and technology. Fuzzy logic is based on the fuzzy set theory (Zadeh, 1965). In the classical set theory the value of membership function of an element to a set is 0 or 1, while in the fuzzy set theory the membership function of an element to a set may have limited or infinite values that are between 0 and 1. Therefore in the fuzzy logic systems partially true premises provide partially true conclusions. The FL system is a simple model of fuzzy logic: connectives, inference rules and examples of inferences will be discussed. Is fuzzy logic against the principles of classical logic or is it an extension of classical logic? Probabilistic and fuzzy logic deals with uncertainty: is probabilistic logic reducible to fuzzy logic? What does it mean? Moreover, I will answer to the question whether Fuzzy logic is or is not the key of the formalization of natural language.

defends fuzzy logic by pointing to what he calls 'misconceptions' concerning fuzzy logic. However, some of these 'misconceptions' are in fact truths, and it is Entemann who has the misconceptions. The present article points to mistakes made by Entemann in three different areas. It closes with a discussion of what sort of general considerations it would take to motivate fuzzy logic.

IEEE Expert, 2000

Fuzzy logic methods have been used successfully in many real-world applications, but the foundations of fuzzy logic remain under attack. Taken together, these two facts constitute a paradox. A second paradox is that almost all of the successful fuzzy logic applications are embedded controllers, while most of the theoretical papers on fuzzy methods deal with knowledge representation and reasoning. I hope here to resolve these paradoxes by identifying which aspects of fuzzy logic render it useful in practice, and which aspects are inessential. My conclusions are based on a mathematical result, on a survey of literature on the use of fuzzy logic in heuristic control and in expert systems, and on practical experience developing expert systems.