Preface This article is written for an intended audience of undergraduate or graduate students, this article provides an introduction to, and an overview of what fuzzy systems are. Presented in this article is an acknowledgment of the contributions that fuzzy systems are making to the Artificial Intelligence discipline, as well as examples of fuzzy systems which are in use today. Abstract The concept and implementation of fuzzy systems is part of the natural course of evolution for humans who are a part of a society where access to information is plentiful but efficient utilization of massive amounts of information is power. To get at the information, we need systems which can understand what we need, rather than for us to understand what information we can ask for. This paper examines how fuzzy systems are not a new concept, but rather an old concept that is a natural part of the evolution of the human race. As society continues to evolve, the implementation and utilization of fuzzy systems will also continue to evolve. Introduction Using the following dictionary[2] definitions provided for the terms "Fuzzy" and "Systems", Fuzzy is defined as "Not clearly worked out; confused" and Systems can be defined as "A set of objects or phenomena grouped together for classification or analysis." A natural question would be, why do we want a confused system that groups together things for analysis? This is an abuse of the literal definition for fuzzy systems. Fuzzy systems can be more correctly defined as a set of objects or phenomena that have been grouped together in a fuzzy way. This type of grouping allows the computers to make judgments and considerations which are considered closer, in response, to the way that humans o... ... middle of paper ... ...re information than previously thought possible and make analysis recommendations. Humans have a fascination for tools which make their lives easier, and fuzzy systems are such a tool. This allows humans more time to continue using the one thing that computers have yet to come us with, the human imagination. References 1. Edwin B. Dean, "Fuzzy Systems from the Perspective of Competitive Advantage", NASA, May 28, 1997 2. "The American Heritage Dictionary, Second College Edition", Houghton Mifflin Company, Boston 1985 3. James F. Brule', "Fuzzy Systems" - A Tutorial 4. Toshiro Terano, Kiyoji Asai, Michio Sugeno, "Fuzzy Systems Theory and Its Applications", 1992, Academic Press, ISBN 0-12-685245-6 5. "Isn't "fuzzy logic" an inherent contradiction?", April 15, 1993, Only two paragraphs, but contains a good example for the value of fuzzy logic.
Reyna, V. F. (2012). A new intuitionism: Meaning, Memory, and Development in Fuzzy-Trace Theory. Judgment and Decision Making, 7(3), 332-359.
... middle of paper ... ... In Intelligent Data Engineering and Automated Learning–IDEAL 2006 (pp. 1346-1357. Springer Berlin, Heidelberg.
Berkeley, George. A Treatise Concerning the Principles of Human Knowledge. 1710. Ed. Kenneth Winkler. Indianapolis: Hackett, 1982.
Kurzweil, Ray. "Reinventing Humanity: the Future of Machine-human Intelligence." The Futurist 1 Mar. 2006. Print.
Artificial intelligence has come a long way since the first robot. In 1950, Alan Turing of Britain publishes, Computer Machinery and Intelligence. This book was proposed to be the birth of artificial intelligence as we know it. The first robot that presents the usage of artificial intelligence was built in 1969. The purpose of this robot was to try out navigation using basic tools such as cameras and bump sensors (Marshall 371). Since then, we have made a million robots way better than this one and we’re going to continue doing so. While the world advances, so is technology. It’s slowly progressing and become better and more reliable. Artificial intelligence is a certain type of technology that is resourceful to our nation. We are using it in the medical field, it’s been helpful to military forces, and it’s helping our world become a better place.
Key Words; Artificial Intelligence, Multiple Intelligence, Fuzzy Logic, Fuzzy Logic Toolbox, Vocational Guidance, Decision Making
The case based reasoning system proposed here mimics the human decision making process by learning from previous experience and using the knowledge to solve current problem. This system will utilize previous adverse episodes and their solutions to prevent reoccurrences, and also to detect the oc...
The traditional notion that seeks to compare human minds, with all its intricacies and biochemical functions, to that of artificially programmed digital computers, is self-defeating and it should be discredited in dialogs regarding the theory of artificial intelligence. This traditional notion is akin to comparing, in crude terms, cars and aeroplanes or ice cream and cream cheese. Human mental states are caused by various behaviours of elements in the brain, and these behaviours in are adjudged by the biochemical composition of our brains, which are responsible for our thoughts and functions. When we discuss mental states of systems it is important to distinguish between human brains and that of any natural or artificial organisms which is said to have central processing systems (i.e. brains of chimpanzees, microchips etc.). Although various similarities may exist between those systems in terms of functions and behaviourism, the intrinsic intentionality within those systems differ extensively. Although it may not be possible to prove that whether or not mental states exist at all in systems other than our own, in this paper I will strive to present arguments that a machine that computes and responds to inputs does indeed have a state of mind, but one that does not necessarily result in a form of mentality. This paper will discuss how the states and intentionality of digital computers are different from the states of human brains and yet they are indeed states of a mind resulting from various functions in their central processing systems.
Crevier, D. (1999). AI: The tumultuous history of the search for Artificial Intelligence. Basic Books: New York.
A major component of Data science is working with statistical models, such as machine learning algorithms in order to transform the data into useful information. The systems thinking the approach will improve machine learning models, and expand applications for big data. A primary example of how this is being implemented in a real-world situation is the deer population in Yellowstone. Park rangers collect were able to collect detailed data about the development of the ecosystem by analyzing the history of the park. Park rangers noticed that the grassland in the park was being destroyed, due to a growing deer population. Therefore by using the data collected and applying a systems thinking approach, they were able to come up with a strategy of introducing wolves into the park to tackle to solve this problem. Ultimately, the introduction of the wolves was able to save the grassland in the park. Systems thinking can be used in various industries, such as having engineers use systems thinking to make improvements, such as analyzing and finding the most efficient ratio between fuel input and output motion. System thinking can be one of the essential tools for various
All of the ways that humans gain information are mimicked by computers. Humans then proceed to analyze and store the information accordingly. This is a computer's main function in today's society. Humans then take all of this information and solve problems logically. This is where things get complex.
Artificial neural networks are systems implemented on computer systems as specialized hardware or sophisticated software that loosely model the learning and remembering functions of the human brain. They are an attempt to simulate the multiple layers of processing elements in the brain, called neurons. These elements are implemented in such a way so that the layers can learn from prior experience and remember their outputs. In this way, the system can learn to recognize certain patterns and situations and apply these to certain priorities and output appropriate results. These types of neural networks can be used in many important situations such as priority in an emergency room, for financial assistance, and any type of pattern recognition such as handwritten or text-to-speech recognition.
Artificial Intelligence is the scientific theory to advance the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. This is going to hold the key in the future. It has always fa...
Williams, Gray ?Robots and Automation.? The new book of popular science. Grolier Inc., 1996, 186-94.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.