Fuzzy Logic
Fuzzy Logic is a term used to identify a new trend of quantifying partial truths. One disadvantage of most rule sets that they cannot process inconsistent data. Fuzzy logic is a superset of conventional logic that has been extended to handle the concept of partial truth, being values that lie between "completely true" and "completely untrue". Dr. Lotfi Zadeth of UC/Berkley first introduced it in the 1960s as a means of modeling the uncertainty of natural language.
All this works by using expanding on Boolean logic and the concept of subsets. In classical set theory, a subset X of a set Y can be defined as a mapping from the elements of Y to the elements of the set (0,1), X: Y --* (0,1). This mapping can be represented by a set of ordered pairs, with one ordered pair present for each element of the set X, and the second element is an element of the set (0,1). The value 0 represents non-membership, and the value 1 is used to represent membership. This approach is limited to only two opposing possible outcomes. If a variable z is in X, then the statement is true if z=1 and false if z=0.
Fuzzy logic takes this process a few steps further and quantifies the degree of membership instead of stopping at a positive or negative correlation. A fuzzy subset F of a set Y can be defined as a set of ordered pairs, each with the first element from S, and the second element from the interval [0,1], with exactly one ordered pair present for each element of S. This defines a mapping between elements of the set S and values in the interval [0,1]. The value zero is still used to represent complete non-membership, the value one is used to represent complete membership, and values in between are used to represent int...
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...sets to airline navigation to stock market predictions. It will be the mother of AI and the stepping-stone for super advanced data processing systems.
Improvement Recommendation
We might have expanded a little on the mathematical explanation of fuzzy logic. It is a very complex application when it comes to large number sets and quantification of extremely diverse rule sets. Other than that, we might have explained how exactly some of the fuzzy logic systems are adapted to some of the real life applications being used today.
References
www.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq.html
www.austinlinks.com/Fuzzy/
www.ortech-engr.com/fuzzy/reservoir.html
www.cs.tamu.edu/research/CFL/
www.emsl.pnl.gov:2080/proj/neuron/fuzzy/what.html
www.fuzzytech.com
www.fuzzy.qgl.org/
www.wolfram.com/products/applications/fuzzylogic
Mill, J. S. (2000). System of Logic Ratiocinative and Inductive. London: Longmans, Green, and Co.
Eventually, AI will be able to think and have emotional responses thanks to the info gathered by
Proposition is a statement that expresses a statement that is uncertain. It could be true or false.
Gottlieb invented modern quantificational logic, and created the first fully axiomatic system for logic, which was complete in its treatment of propositional and first-order logic, and also represented the first treatment of higher-order logic. In the philosophy of mathematics, he was one of the most vital contributor of logicism, the thesis that mathematical truths are logical truths, and presented influential criticisms of rival views such as psychologism and formalism.
We thought of using this but needed a binary opposite to go in it so
As our research into science and technology ever increases its seems inevitable that in the near future Artificial Intelligent machines will exist and become part of our everyday life such as we see with modern computers today.
Today, computer scientists often use Boolean to relate sets of data. Using the words AND, OR, NOT, IF, and EXCEPT, a Boolean algebraist can determine the truth of a statement as well as rewrite the statement in a more understandable form.
In this paper, I offer a solution to the Gettier problem by adding a fourth condition to the justified true belief analysis of knowledge. First though, a brief review. Traditionally, knowledge had been accounted for with the justified true belief analysis. To know something, three conditions had to be met: first, you had to have a belief; second, the belief had to be justified; third, this justified belief had to be true. So a justified true belief counts as knowledge. Gettier however showed this analysis to be inadequate as one can have a justified true belief that no one would want to count as knowledge.
Most of the day the human mind is taking in information, analyzing it, storing it accordingly, and recalling past knowledge to solve problems logically. This is similar to the life of any computer. Humans gain information through the senses. Computers gain similar information through a video camera, a microphone, a touch pad or screen, and it is even possible for computers to analyze scent and chemicals. Humans also gain information through books, other people, and even computers, all of which computers can access through software, interfacing, and modems. For the past year speech recognition software products have become mainstream(Lyons,176). 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. There are expert systems that can solve complex problems that humans train their whole lives for. In 1997, IBM's Deep Blue defeated the world champion in a game of chess(Karlgaard, p43). Expert systems design buildings, configure airplanes, and diagnose breathing problems. NASA's Deep Space One probe left with software that lets the probe diagnose problems and fix itself(Lyons).
According to traditional syllogistic logic, which has its roots in Aristotle, there are four types of propositions: the A proposition ("All S are P"), the E proposition ("No S are P"), the I proposition ("Some S are P"), and the O proposition ("Some S are not P"). These propositional types represent all of the possible combinations of the dichotomies of affirmative/negative and universal/particular. Each makes a claim that a certain essent (the particular I and O propositions) or an entire class of essents (the universal A and E propositions), the subject or subject-class, relates in some way (belongs or does not belong) to a class of essents designated by the predicate of the proposition. The traditional, or Aristotelian, standpoint for evaluating the truth or falsity of these propositions assumes that each class designated by a term in the subject and predicate actually exists. This allows certain conclusions to be drawn regarding the relationship between the truth values about different types of propositions, and these relationships are symbolized visually in a diagram called the "Traditional Square of Opposition." (These relationships are designated as "contradictory," "subalternate," "contrary," and "subcontrary.") The modern, or Boolean, interpretation of Aristotle's syllogistic logic, however, makes no assumptions about the existence of the classes denoted by the subject and predicate terms of a proposition. Because of this, there are fewer conclusions that one is able to draw about the relationships between the truth-values of different propositions. (The only relationship on the modern square of opposition is the relationship denoted by the term "contradictory....
It is a type of artificial intelligence program that imitated the analytical skills and understanding of human experts. By 1985, the artificial intelligence market had come up to one billion dollars; moreover, around the same time, Japan’s fifth generation computer project motivated the British and American government to bring back funding for artificial intelligence. Unfortunately, the artificial intelligence market fell back into disrepute which started with the fall of the Lisp Machine market. Additionally, this was a much longer “AI winter”. Soon, in the late 1900s and in the beginning of the 21st century, artificial intelligence was starting to be utilized for data mining, medical diagnosis, and in other areas as well as logistics. All this success was because of the increasing computational power, new relationships between other fields and artificial intelligence, higher significance on answering specific issues, and a commitment by researchers to scientific standards as well as mathematical methods. For example, on May 11th, 1997, Deep Blue (an IBM computer) was the first computer that played chess and it beat the ruling world chess champion at that time, Garry Kasparov. This was the beginning of an amazing discovery, artificial intelligence. Faster computers, able to obtain huge amounts of information, and statistical and advanced methods allowed progress in perception and machine learning. By the midyear of 2010, machine learning programs were utilized in the entire world. For example, Watson (IBM’s question answering system) beat Ken Jennings and Brad Rutter, the two greatest champions of Jeopardy, in a Jeopardy exhibition match by huge amounts. Another example is of the Kinect. It gives a 3D body-motion interface for the Xbox One and the Xbox 360 using algorithms that surfaced from long artificial research. Soon, 2015 came. According to
In the 1950s, Norbert Wiener made an observation on feedback theory, that all intelligent behavior is the result of feedback machines. The best example of a feedback machine would be a thermostat, which records the temperature in the room and compares it to the wanted temperature, and then changes the heat based on the difference between the two. Also in 1950, Alan Turing developed the Turing Test. A person asks another human, called the foil, and a computer questions via keyboard and screen, then try to tell which one is the computer. This is repeated with different foils and questioners, if many people cannot tell the difference between the two, then the computer can be considered intelligent. In 1955 Herbert Simon and Alan Newell developed The Logic Theorist, which is considered the first AI program. It would visualise the problem given as a tree diagram, then choose the branch that is most likely to solve the problem. In 1956, John Mccarthy organized a conference he called “The Dartmouth summer research project on Artificial Intelligence.” Though the conference was not successful, the topics discussed there led to more research later on. From then on, this fiel...
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...
The logic used to explain miracles of everyday life, thinking logically helps man to question the functioning of everything around us, the logic used to argue and is somehow a thought an idea that influences us for an action we do in our daily lives.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?