Basically, it's a very general model. Your classes will probably emphasize the link between automata, languages and logic. If I were looking at relating this to concrete "worldly" tools, I'd spend a leisurely morning at the library reading a couple parts A-B?
Of course it's just one of the many ways of looking for applications of an automata class, and I guess not the most obvious - but that's precisely why it's an interesting exercise.
I also use the setting to teach Python's almost purely functional subset, including maps, lambdas, and set comprehensions, using which one can code up standard FA algorithms, often in a manner virtually indistinguishable from math defns. There has been considerable research related to automata theory in model checking used in the industry.
Check Moshe Vardi's recent lectures at Fields Institute , in particular the 3rd lecture "Logic, Automata, Games, and Algorithms" for a taste of why automata theory is still important and useful. The automata-theoretic approach to decision procedures, introduced by Buechi, Elgot, Rabin and Trakhtenbrot in the s and s, is one of the most fundamental approaches to decision procedures.
Recently, this approach has found industrial applications in formal verification of hardware and software systems. The path from logic to practical algorithms goes not only through automata, but also through games, whose algorithmic aspects were studies by Chandra, Kozen, and Stockmeyer in the late s.
In this overview talk we describe the path from logic to algorithms via automata and games. The slides and audio files of lectures are available here: 1 , 2 , 3. They turn out to provide an excellent model for encapsulating character behavior; for instance, an enemy might have states representing 'patrol', 'search', 'approach', 'attack', 'defend', 'retreat', 'die', etc.
This doesn't involve any of the formal aspects of automata like regular languages and the like, but the concept of the automaton is a very core one. We have seen that the language which contrasts theory and practice, setting the one above the other, is the very consummation of ignorance—that it proves a man to be unacquainted with the very first elements of thought, and goes a great way towards proving his mind to be so perverted as to be incapable of being taught them.
We should take into account the semantics of the words "practical" and "application". For some students, practical is anything that will help them pass their exams; for others, anything that will come up in a job. In both cases, Automata Theory is very practical indeed. As others point out, you will use grammars, for example, when studying compilers.
But even more than that: understanding the whole concept of having different states and rules for transitions between them can make you a better programmer when you realize, for example, that your code is redundant here and there, and that when you improve it, you are applying in your code the same conceptual ideas behind DFA minimization.
Similarly for "application". What do you understand by that word? Even if you are a "down-to-earth engineer" you will see and use ideas similar to those of Automata Theory in real world projects: programming code, flow diagrams, and even the simple yet brilliant concept of a stack. For theory nerds like me, I consider applications of Automata Theory in other areas, like logic, algebra and finite model theory. Surely, I will probably never need to use the pumping lemma while shopping in a supermarket, but theorems like that have helped me understand the structure of certain classes of languages, not to mention the logics and algebraics structures they are in correspondence with.
And that is something I value more than any measure of practicality. Considering the automata side of things leads to nice algorithms. Finite Automata, often written about as finite state machines in different contexts, or with their probabilistic variants Hidden Markov Models can be applied to pattern recognition and quantifying structure of a pattern.
See for example CSSR , an algorithm for blindly reconstruction hidden states; it's more efficient and flexible than Hidden Markov Models. Another more practical application of automata theory is the development of artificial intelligence. Artificial Intelligence was developed from the concept of finite automaton. The neural network of robots is constructed on the basis of automata theory.
After all robots are also automata. Some have given great answers when it comes to how it relates to industry. What should be important is its scientific value, and Automata theory is often the doorway to first understand a higher tier of theory of computation in an undergraduate student's studies. Automata theory has a grand set of theorems that pop up all over the place in Theoretical Computer Science, and especially when one wants to talk about application such as Compilers.
Its scientific value its not outdated, how could it be? It's core theory to the field. It is practical as it is knowledge that is useful to those who understand or want to understand the nature of computation. If you cannot find use in it, I question ones research or even intent to study CS as it's not programming that's an application of CS , it's a formal science.
Sign up to join this community. The best answers are voted up and rise to the top. From this idea, one can defne the complexity of a language, which can be classified as P or NP , exponential , or probabilistic , for example. Noam Chomsky extended the automata theory idea of complexity hierarchy to a formal language hierarchy , which led to the concept of formal grammar. A formal grammar system is a kind of automata specifically defined for linguistic purposes.
The parameters of formal grammar are generally defined as:. As in purely mathematical automata, grammar automata can produce a wide variety of complex languages from only a few symbols and a few production rules.
Chomsky's hierarchy defines four nested classes of languages, where the more precise aclasses have stricter limitations on their grammatical production rules. The formality of automata theory can be applied to the analysis and manipulation of actual human language as well as the development of human-computer interaction HCI and artificial intelligence AI.
To the casual observer, biology is an impossibly complex science. Traditionally, the intricacy and variation found in life science has been attributed to the notion of natural selection. Species become "intentionally" complex because it increases their chance for survival. For example, a camoflauge-patterned toad will have a far lower risk of being eaten by a python than a frog colored entirely in orange.
This idea makes sense, but automata theory offers a simpler and more logical explanation, one that relies not on random, optimizing mutations but on a simple set of rules. Basic automata theory shows that simplicity can naturally generate complexity.
Apparent randomness in a system results only from inherent complexities in the behavior of automata, and seemingly endless variations in outcome are only the products of different initial states. The arrow entering from the left into q 0 shows that q 0 is the initial state of the machine.
Moves that do not involve changes of states are indicated by arrows along the sides of individual nodes. These arrows are known as self-loops. There exist several types of finite-state machines , which can be divided into three main categories:. Applications of finite-state machines are found in a variety of subjects.
The simplest automata used for computation is a finite automaton. It can compute only very primitive functions; therefore, it is not an adequate computation model. In addition, a finite-state machine's inability to generalize computations hinders its power. The following is an example to illustrate the difference between a finite-state machine and a Turing machine:.
Imagine a Modern CPU. Every bit in a machine can only be in two states 0 or 1. Therefore, there are a finite number of possible states. In addition, when considering the parts of a computer a CPU interacts with, there are a finite number of possible inputs from the computer's mouse, keyboard, hard disk, different slot cards, etc. As a result, one can conclude that a CPU can be modeled as a finite-state machine. Now, consider a computer. Although every bit in a machine can only be in two different states 0 or 1 , there are an infinite number of interactions within the computer as a whole.
It becomes exceeding difficult to model the workings of a computer within the constraints of a finite-state machine. However, higher-level, infinite and more powerful automata would be capable of carrying out this task. World-renowned computer scientist Alan Turing conceived the first "infinite" or unbounded model of computation: the Turing machine, in , to solve the Entscheindungsproblem.
What are uses of theory of automata in field of CS? Which language is accepted by finite automata? Why we are using automata in compiler construction? What are the phases of compiler? What are the roles of finite automata in compiler? What is the full form of DFA? What is parser role?
What is the role of lexical analyzer? What is meant by lexical analyzer? What is parsing and role of lexical analyzer? What lexeme means? What is lexeme example? Which one is type of lexeme?
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