Automata?
It is plural of automaton simply define as "something that works automatically".
Example Of Automaton:
• Computer
Automata is concerned with mathematical models which are foundations of computing. These theoretical concepts will not vary with the next new model of computers.
A firm knowledge of the theoretical basis for computation will give you a stable platform from which to observes and understand the dazzling progress being made in the production of new computers and software..
History Of Automata:
In the 1930’s, Alan Turing (1912 – 1952), an English mathematician, studied an abstracts machine called Turing machine even before computers existed!
• He is regarded as pioneer of automata theory
Turing Machine:
The goal
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In the 1940’s and 1950’s, machines currently called “Finite Automata” were studied by a number of researchers:
• Initially proposed to model the brain function.
• Later used for variety of others purposes. 2. In late 1950’s, the Linguist N. Chomsky introduced formal grammar.
• Has close relationship to abstracts automata.
• Also important in development of software components and compiler. 3. In 1969, S. Cook extended the theory of Turing:
• what could be solved and what couldn’t.
Computability:
• S. Cook separated the solvable problems from those that can in principles be solved by a computer, but in practice, take so much time that computers are useless for all but very small instances of these problems.
• Latter class of problems are called “intractable or NP-hard.
• Complexity of Problems
Description Of Automata Theory:
Automata Theory is an interesting and in theoretical branch of computer science .Automata is the study of "abstract" computing devices machines and their algorithms.
• Also called theory of computation .irrelevant complications are dropped in order to focus on important concept.
• Automata theory help ensure the safety critical systems are correct.
• It helps create abstract models for
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•Length of string w is denoted as |w|
•Let w = 10011
–|w| = 5
–|ϵ| = ?
•0
–x = 01 ϵ 0 ϵ 1 ϵ 00 ϵ |x| = ?
•6
•xy = concatenation of two strings x and y
•ϵ being the identity for concatenation
•x is said to be prefix of y if xz = y for some z.
•z is said to be suffix of x if xz = y.
Language And Grammar:
•A language is a set of words.
•E.g.
–Given Σ = {0,1}, we may define a language L = {00, 01,10, - - -}.
•A grammar is a finite list of rules defining a language.
–Enumerates words of a language - nothing more, nothing less.
Powers Of An Alphabet:
•Σk = the set of all strings of length k
•E.g. Σ = {a, b, c}
–Σ1 = ? •{a, b, c}
–Σ2 = ?
•{aa, ab, ac, ba, bb, bc, ca, cb, cc}
–Σ0 = ? •{ϵ}
•The set of all strings over an alphabet Σ is denoted by Σ*
–Σ* = Σ0 U Σ1 U Σ2 U …
•E.g. Σ = {0, 1}, then Σ* = ?
–Σ* = {ϵ, 0, 1, 00, 01, 10, 11, 000, . . . } •The set of all non-empty strings over an alphabet Σ is denoted by Σ+
–Σ+ = Σ1 U Σ2 U … or equivalently,
–Σ* = {ϵ} U Σ+
Language:
•L is said to be a language over alphabet Σ only if L Σ*
•Example
–Let L (defined over Σ = {0, 1}), be the language of all strings consisting of n 0’s followed by n
All languages could be successfully analyzed in terms of mathematical equations. In this sense, language is mathematics. This thesis enables us to explain why languages usually have different word orders, and why any language could be highly flexible.
Andy Clark strongly argues for the theory that computers have the potential for being intelligent beings in his work “Mindware: Meat Machines.” The support Clark uses to defend his claims states the similar comparison of humans and machines using an array of symbols to perform functions. The main argument of his work can be interpreted as follows:
In this paper I will evaluate and present A.M. Turing’s test for machine intelligence and describe how the test works. I will explain how the Turing test is a good way to answer if machines can think. I will also discuss Objection (4) the argument from Consciousness and Objection (6) Lady Lovelace’s Objection and how Turing responded to both of the objections. And lastly, I will give my opinion on about the Turing test and if the test is a good way to answer if a machine can think.
According to Descartes, non-human animals are automata, which imply that their behavior is completely explicable with regards to physical mechanisms (Kirk, 2011). The philosopher explored the concept of a machine that looked and behaved like a human being. Following his attempts to unmask such a machine, Descartes concluded that no machine could behave like a human being and that characteristically explaining human behavior needed something beyond the phy...
One of the hottest topics that modern science has been focusing on for a long time is the field of artificial intelligence, the study of intelligence in machines or, according to Minsky, “the science of making machines do things that would require intelligence if done by men”.(qtd in Copeland 1). Artificial Intelligence has a lot of applications and is used in many areas. “We often don’t notice it but AI is all around us. It is present in computer games, in the cruise control in our cars and the servers that route our email.” (BBC 1). Different goals have been set for the science of Artificial Intelligence, but according to Whitby the most mentioned idea about the goal of AI is provided by the Turing Test. This test is also called the imitation game, since it is basically a game in which a computer imitates a conversating human. In an analysis of the Turing Test I will focus on its features, its historical background and the evaluation of its validity and importance.
The Turing Machine is a simple kind of computer. It is limited to reading and writing symbols on a tape and moving the tape along to the left or right. The tape is marke...
In the 1940s and 1950s scientists began to discuss the possibility of creating an artificial brain. Research sped up after neurologists discovered that the brain is an electrical network of neurons. Then, in 1950, Alan Turing published a paper in which he discussed the possibility of creating machines that think. Since "thinking" is difficult to define, he created the “Turing Test.” The test stated that a machine could “think” if it was able to carry on a teleprinter conversation that was indistinguishable from a human
Artificial intelligence folklore has been traced back to the times of Ancient Egypt. But the "birth of artificial intelligence" as some would call it, was in 1956 at the Dartmouth conference. The conference was based on two theories, the principle of feedback theory and the Logic Theorist. The principle of feedback theory was observed by Norbert Wiener. He theorized that all intelligent behavior was the result of a feedback mechanism. An example would be a temperature control system that simply checks the temperature of the room, compares the reading to the desired temperature, and adjusts the flow of heat to bring the room to the desired temperature. Then in 1955, Newell and Simon developed The Logic Theorist. The Logic Theorist was a program that represented every problem as a tree. The program would attempt to solve a problem by selecting the branch that would most likely result in the correct solution. Then in 1956, John McCarthy1 organized the Dartmouth Conference to draw interest and talent to the field of artificial intelligence.2
The website for Princeton University’s Computer Science department offers a great analogy of the subject, “What energy is to physics, information is to computer science.”
Although the majority of people cannot imagine life without computers, they owe their gratitude toward an algorithm machine developed seventy to eighty years ago. Although the enormous size and primitive form of the object might appear completely unrelated to modern technology, its importance cannot be over-stated. Not only did the Turing Machine help the Allies win World War II, but it also laid the foundation for all computers that are in use today. The machine also helped its creator, Alan Turing, to design more advanced devices that still cause discussion and controversy today. The Turing Machine serves as a testament to the ingenuity of its creator, the potential of technology, and the glory of innovation.
The legitimacy of apes’ understanding of human language is a much-deliberated topic. Though many apes have been trained to understand and use American Sign Language, the degree to which they exhibit comprehension of the properties of human language seems to vary. The apes Sherman and Austin, were able to use symbols to describe objects that were not immediately present and also to describe their intended actions, which is demonstrative of the displacement property. Sherman and Austin could also look at a certain set of printed lexigram symbols and denote whether each could be classified as either a ‘tool’ or a ‘food.’ Since they were never told beforehand which lexigram symbol corresponded to which classification, the apes were successfully demonstrating the arbitrariness property of language. Apes have even demonstrated making their own language rules, such as using...
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.
...ite number of thoughts, feelings, and more. The infiniteness distinguishes this language from a the finite nature of sound and gestures in that important way, so this type of spoken, written language would still be the first of its sort that is acquired.
For a regular language, for every i, x z should be in language L1 where i>=0
The history of the computer dates back all the way to the prehistoric times. The first step towards the development of the computer, the abacus, was developed in Babylonia in 500 B.C. and functioned as a simple counting tool. It was not until thousands of years later that the first calculator was produced. In 1623, the first mechanical calculator was invented by Wilhelm Schikard, the “Calculating Clock,” as it was often referred to as, “performed it’s operations by wheels, which worked similar to a car’s odometer” (Evolution, 1). Still, there had not yet been anything invented that could even be characterized as a computer. Finally, in 1625 the slide rule was created becoming “the first analog computer of the modern ages” (Evolution, 1). One of the biggest breakthroughs came from by Blaise Pascal in 1642, who invented a mechanical calculator whose main function was adding and subtracting numbers. Years later, Gottfried Leibnez improved Pascal’s model by allowing it to also perform such operations as multiplying, dividing, taking the square root.