No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Abstract
The main concern of this chapter is to provide a brief description of artificial intelligence (AI) in terms of historical evolution, types, education, human intelligence, methods, rationality, and related aspects. In the principles that trigger human intelligence, three stages are explained in detail; imagination, visualization, and idea generation. After these three stages of reflection, it is recommended that critical discussion and experimental validation are important cornerstones for scientific inferences. Based on the author’s experience, a number of important recommendations are proposed for future AI work. As will be explained in the next section, before mathematical equations, it is important to understand the solution mechanism of any problem linguistically based on philosophical and logical rule principles. Finally, some examples of misuse in AI studies are given.KeywordsArtificialEducationEngineeringHumanIntelligenceLinguisticNaturalRobotScienceTechnology
ResearchGate has not been able to resolve any citations for this publication.
I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
The approach described in this paper represents a substantive departure from the conventional quantitative techniques of system analysis. It has three main distinguishing features: 1) use of so-called ``linguistic'' variables in place of or in addition to numerical variables; 2) characterization of simple relations between variables by fuzzy conditional statements; and 3) characterization of complex relations by fuzzy algorithms. A linguistic variable is defined as a variable whose values are sentences in a natural or artificial language. Thus, if tall, not tall, very tall, very very tall, etc. are values of height, then height is a linguistic variable. Fuzzy conditional statements are expressions of the form IF A THEN B, where A and B have fuzzy meaning, e.g., IF x is small THEN y is large, where small and large are viewed as labels of fuzzy sets. A fuzzy algorithm is an ordered sequence of instructions which may contain fuzzy assignment and conditional statements, e.g., x = very small, IF x is small THEN Y is large. The execution of such instructions is governed by the compositional rule of inference and the rule of the preponderant alternative. By relying on the use of linguistic variables and fuzzy algorithms, the approach provides an approximate and yet effective means of describing the behavior of systems which are too complex or too ill-defined to admit of precise mathematical analysis.
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
To answer the questions of how information about the physical world is sensed, in what form is information remembered, and how does information retained in memory influence recognition and behavior, a theory is developed for a hypothetical nervous system called a perceptron. The theory serves as a bridge between biophysics and psychology. It is possible to predict learning curves from neurological variables and vice versa. The quantitative statistical approach is fruitful in the understanding of the organization of cognitive systems. 18 references.
this paper in L a T E Xpartly supported by ARPA (ONR) grant N00014-94-1-0775 to Stanford University where John McCarthy has been since 1962. Copied with minor notational changes from CACM, April 1960. If you want the exact typography, look there. Current address, John McCarthy, Computer Science Department, Stanford, CA 94305, (email: jmc@cs.stanford.edu), (URL: http://www-formal.stanford.edu/jmc/ ) by starting with the class of expressions called S-expressions and the functions called S-functions. In this article, we first describe a formalism for defining functions recursively. We believe this formalism has advantages both as a programming language and as a vehicle for developing a theory of computation. Next, we describe S-expressions and S-functions, give some examples, and then describe the universal S-function apply which plays the theoretical role of a universal Turing machine and the practical role of an interpreter. Then we describe the representation of S-expressions in the memory of the IBM 704 by list structures similar to those used by Newell, Shaw and Simon [2], and the representation of S-functions by program. Then we mention the main features of the LISP programming system for the IBM 704. Next comes another way of describing computations with symbolic expressions, and finally we give a recursive function interpretation of flow charts. We hope to describe some of the symbolic computations for which LISP has been used in another paper, and also to give elsewhere some applications of our recursive function formalism to mathematical logic and to the problem of mechanical theorem proving. 2 Functions and Function Definitions
Genetik Algoritmalar (Genetic algorithms)
- M H Satman
- MH Satman
Yapay Sinir Ağları İlkeleri. (Artificial neural network principles)
- Z Şen
Genetik Algoritmalar (Genetic algorithms). Türkmen Kitapevi, Turkish
- M H Satman
Genetik Algoritmalar ve En İyileme Yöntemleri. (Genetic algorithms and optimization methods)
- Z Şen
Machines who think, Language, scenes, symbols and understanding
- P Mccorduck
Bulanık Mantık İlkeleri ve Modelleme (Mühendislik ve Sosyal Bilimler) (Fuzzy logic principles and Modeling - engineering and social sciences)
- Z Şen