A regular expression, regex or regexp is a sequence of characters that define a search pattern. This pattern is used by string searching algorithms for "find" or "find and replace" operations on strings, or for input validation, it is a technique developed in formal language theory. The concept arose in the 1950s when the American mathematician Stephen Cole Kleene formalized the description of a regular language; the concept came into common use with Unix text-processing utilities. Since the 1980s, different syntaxes for writing regular expressions exist, one being the POSIX standard and another used, being the Perl syntax. Regular expressions are used in search engines and replace dialogs of word processors and text editors, in text processing utilities such as sed and AWK and in lexical analysis. Many programming languages provide regex capabilities, built-in or via libraries; the phrase regular expressions, regexes, is used to mean the specific, standard textual syntax for representing patterns for matching text.
Each character in a regular expression is either a metacharacter, having a special meaning, or a regular character that has a literal meaning. For example, in the regex a. A is a literal character which matches just'a', while'.' is a meta character that matches every character except a newline. Therefore, this regex matches, for example,'a', or'ax', or'a0'. Together and literal characters can be used to identify text of a given pattern, or process a number of instances of it. Pattern matches may vary from a precise equality to a general similarity, as controlled by the metacharacters. For example. Is a general pattern, is less general and a is a precise pattern; the metacharacter syntax is designed to represent prescribed targets in a concise and flexible way to direct the automation of text processing of a variety of input data, in a form easy to type using a standard ASCII keyboard. A simple case of a regular expression in this syntax is to locate a word spelled two different ways in a text editor, the regular expression serialie matches both "serialise" and "serialize".
Wildcards achieve this, but are more limited in what they can pattern, as they have fewer metacharacters and a simple language-base. The usual context of wildcard characters is in globbing similar names in a list of files, whereas regexes are employed in applications that pattern-match text strings in general. For example, the regex ^+|+$ matches excess whitespace at the beginning or end of a line. An advanced regular expression that matches any numeral is??. A regex processor translates a regular expression in the above syntax into an internal representation which can be executed and matched against a string representing the text being searched in. One possible approach is the Thompson's construction algorithm to construct a nondeterministic finite automaton, made deterministic and the resulting deterministic finite automaton is run on the target text string to recognize substrings that match the regular expression; the picture shows the NFA scheme N obtained from the regular expression s*, where s denotes a simpler regular expression in turn, recursively translated to the NFA N.
Regular expressions originated in 1951, when mathematician Stephen Cole Kleene described regular languages using his mathematical notation called regular sets. These arose in theoretical computer science, in the subfields of automata theory and the description and classification of formal languages. Other early implementations of pattern matching include the SNOBOL language, which did not use regular expressions, but instead its own pattern matching constructs. Regular expressions entered popular use from 1968 in two uses: pattern matching in a text editor and lexical analysis in a compiler. Among the first appearances of regular expressions in program form was when Ken Thompson built Kleene's notation into the editor QED as a means to match patterns in text files. For speed, Thompson implemented regular expression matching by just-in-time compilation to IBM 7094 code on the Compatible Time-Sharing System, an important early example of JIT compilation, he added this capability to the Unix editor ed, which led to the popular search tool grep's use of regular expressions.
Around the same time when Thompson developed QED, a group of researchers including Douglas T. Ross implemented a tool based on regular expressions, used for lexical analysis in compiler design. Many variations of these original forms of regular expressions were used in Unix programs at Bell Labs in the 1970s, including vi, sed, AWK, expr, in other programs such as Emacs. Regexes were subsequently adopted by a wide range of programs, with these early forms standardized in the POSIX.2 standard in 1992. In the 1980s the more complicated regexes arose in Perl, which derived from a regex library written by Henry Spencer, who wrote an implementation of Advanced Regular Expressions for Tcl; the Tcl library is a hybrid NFA/DFA implementation with improved performance characteristics. Software projects that have adopted Spencer's Tcl regular expression implementation include PostgreSQL. Perl expanded on Spencer's original library
Theoretical computer science
Theoretical computer science is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation. It is difficult to circumscribe the theoretical areas precisely; the ACM's Special Interest Group on Algorithms and Computation Theory provides the following description: TCS covers a wide variety of topics including algorithms, data structures, computational complexity and distributed computation, probabilistic computation, quantum computation, automata theory, information theory, program semantics and verification, machine learning, computational biology, computational economics, computational geometry, computational number theory and algebra. Work in this field is distinguished by its emphasis on mathematical technique and rigor. While logical inference and mathematical proof had existed in 1931 Kurt Gödel proved with his incompleteness theorem that there are fundamental limitations on what statements could be proved or disproved.
These developments have led to the modern study of logic and computability, indeed the field of theoretical computer science as a whole. Information theory was added to the field with a 1948 mathematical theory of communication by Claude Shannon. In the same decade, Donald Hebb introduced a mathematical model of learning in the brain. With mounting biological data supporting this hypothesis with some modification, the fields of neural networks and parallel distributed processing were established. In 1971, Stephen Cook and, working independently, Leonid Levin, proved that there exist relevant problems that are NP-complete – a landmark result in computational complexity theory. With the development of quantum mechanics in the beginning of the 20th century came the concept that mathematical operations could be performed on an entire particle wavefunction. In other words, one could compute functions on multiple states simultaneously; this led to the concept of a quantum computer in the latter half of the 20th century that took off in the 1990s when Peter Shor showed that such methods could be used to factor large numbers in polynomial time, which, if implemented, would render most modern public key cryptography systems uselessly insecure.
Modern theoretical computer science research is based on these basic developments, but includes many other mathematical and interdisciplinary problems that have been posed, as shown below: An algorithm is a step-by-step procedure for calculations. Algorithms are used for calculation, data processing, automated reasoning. An algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Starting from an initial state and initial input, the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states producing "output" and terminating at a final ending state; the transition from one state to the next is not deterministic. A data structure is a particular way of organizing data in a computer so that it can be used efficiently. Different kinds of data structures are suited to different kinds of applications, some are specialized to specific tasks. For example, databases use B-tree indexes for small percentages of data retrieval and compilers and databases use dynamic hash tables as look up tables.
Data structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services. Efficient data structures are key to designing efficient algorithms; some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Storing and retrieving can be carried out on data stored in both main memory and in secondary memory. Computational complexity theory is a branch of the theory of computation that focuses on classifying computational problems according to their inherent difficulty, relating those classes to each other. A computational problem is understood to be a task, in principle amenable to being solved by a computer, equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.
The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are used, such as the amount of communication, the number of gates in a circuit and the number of processors. One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do. Distributed computing studies distributed systems. A distributed system is a software system in which components located on networked computers communicate and coordinate their actions by passing messages; the components interact with each other. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, independent failure of components. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications, and now a perfect example could be the blockcain.
A computer program that runs in a distributed system is called a distributed program, distributed programming is the process of writing such programs. There are m
Computational complexity theory
Computational complexity theory focuses on classifying computational problems according to their inherent difficulty, relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used; the theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying their computational complexity, i.e. the amount of resources needed to solve them, such as time and storage. Other measures of complexity are used, such as the amount of communication, the number of gates in a circuit and the number of processors. One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do; the P versus NP problem, one of the seven Millennium Prize Problems, is dedicated to the field of computational complexity.
Related fields in theoretical computer science are analysis of algorithms and computability theory. A key distinction between analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the amount of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more general question about all possible algorithms that could be used to solve the same problem. More computational complexity theory tries to classify problems that can or cannot be solved with appropriately restricted resources. In turn, imposing restrictions on the available resources is what distinguishes computational complexity from computability theory: the latter theory asks what kind of problems can, in principle, be solved algorithmically. A computational problem can be viewed as an infinite collection of instances together with a solution for every instance; the input string for a computational problem is referred to as a problem instance, should not be confused with the problem itself.
In computational complexity theory, a problem refers to the abstract question to be solved. In contrast, an instance of this problem is a rather concrete utterance, which can serve as the input for a decision problem. For example, consider the problem of primality testing; the instance is a number and the solution is "yes" if the number is prime and "no" otherwise. Stated another way, the instance is a particular input to the problem, the solution is the output corresponding to the given input. To further highlight the difference between a problem and an instance, consider the following instance of the decision version of the traveling salesman problem: Is there a route of at most 2000 kilometres passing through all of Germany's 15 largest cities? The quantitative answer to this particular problem instance is of little use for solving other instances of the problem, such as asking for a round trip through all sites in Milan whose total length is at most 10 km. For this reason, complexity theory addresses computational problems and not particular problem instances.
When considering computational problems, a problem instance is a string over an alphabet. The alphabet is taken to be the binary alphabet, thus the strings are bitstrings; as in a real-world computer, mathematical objects other than bitstrings must be suitably encoded. For example, integers can be represented in binary notation, graphs can be encoded directly via their adjacency matrices, or by encoding their adjacency lists in binary. Though some proofs of complexity-theoretic theorems assume some concrete choice of input encoding, one tries to keep the discussion abstract enough to be independent of the choice of encoding; this can be achieved by ensuring that different representations can be transformed into each other efficiently. Decision problems are one of the central objects of study in computational complexity theory. A decision problem is a special type of computational problem whose answer is either yes or no, or alternately either 1 or 0. A decision problem can be viewed as a formal language, where the members of the language are instances whose output is yes, the non-members are those instances whose output is no.
The objective is to decide, with the aid of an algorithm, whether a given input string is a member of the formal language under consideration. If the algorithm deciding this problem returns the answer yes, the algorithm is said to accept the input string, otherwise it is said to reject the input. An example of a decision problem is the following; the input is an arbitrary graph. The problem consists in deciding; the formal language associated with this decision problem is the set of all connected graphs — to obtain a precise definition of this language, one has to decide how graphs are encoded as binary strings. A function problem is a computational problem where a single output is expected for every input, but the output is more complex than that of a decision problem—that is, the output isn't just yes or no. Notable examples include the integer factorization problem, it is tempting to think that the notion of function problems is much richer than the notion of decision problems. However, this is not the case, since function problems can be recast as decision problems.
For example, the multiplication of two integers can be expressed as the set of triples such that the relation a × b = c holds. Deciding whether a given triple is a member of this set corresponds to solving
In automata theory, DFA minimization is the task of transforming a given deterministic finite automaton into an equivalent DFA that has a minimum number of states. Here, two DFAs are called equivalent. Several different algorithms accomplishing this task are known and described in standard textbooks on automata theory. For each regular language, there exists a minimal automaton that accepts it, that is, a DFA with a minimum number of states and this DFA is unique; the minimal DFA ensures minimal computational cost for tasks such as pattern matching. There are two classes of states that can be removed or merged from the original DFA without affecting the language it accepts to minimize it. Unreachable states are the states that are not reachable from the initial state of the DFA, for any input string. Nondistinguishable states are those that cannot be distinguished from one another for any input string. DFA minimization is done in three steps, corresponding to the removal or merger of the relevant states.
Since the elimination of nondistinguishable states is computationally the most expensive one, it is done as the last step. The state p of DFA M= is unreachable if no such string w in Σ* exists for which p=δ*. Reachable states can be obtained with the following algorithm: Unreachable states can be removed from the DFA without affecting the language that it accepts. One algorithm for merging the nondistinguishable states of a DFA, due to Hopcroft, is based on partition refinement, partitioning the DFA states into groups by their behavior; these groups represent equivalence classes of the Myhill–Nerode equivalence relation, whereby every two states of the same partition are equivalent if they have the same behavior for all the input sequences. That is, for every two states p1 and p2 that belong to the same equivalence class within the partition P, every input word w, the transitions determined by w should always take states p1 and p2 to equal states, states that both accept, or states that both reject.
It should not be possible for w to take p1 to an accepting state and p2 to a rejecting state or vice versa. The following pseudocode describes the algorithm: The algorithm starts with a partition, too coarse: every pair of states that are equivalent according to the Myhill–Nerode relation belong to the same set in the partition, but pairs that are inequivalent might belong to the same set, it refines the partition into a larger number of smaller sets, at each step splitting sets of states into pairs of subsets that are inequivalent. The initial partition is a separation of the states into two subsets of states that do not have the same behavior as each other: the accepting states and the rejecting states; the algorithm repeatedly chooses a set A from the current partition and an input symbol c, splits each of the sets of the partition into two subsets: the subset of states that lead to A on input symbol c, the subset of states that do not lead to A. Since A is known to have different behavior than the other sets of the partition, the subsets that lead to A have different behavior than the subsets that do not lead to A.
When no more splits of this type can be found, the algorithm terminates. Lemma. Given a fixed character c and an equivalence class Y that splits into equivalence classes B and C, only one of B or C is necessary to refine the whole partition. Example: Suppose we have an equivalence class Y that splits into equivalence classes B and C. Suppose we have classes D, E, F. By the Lemma, we can choose either B or C as the distinguisher, let's say B; the states of D and E are split by their transitions into B. But F, which doesn't point into B doesn't split during the current iteration of the algorithm. Observation. All of B or C is necessary to split referring classes like D, E, F correctly-- subsets won't do; the purpose of the outermost if statement is to patch up W, the set of distinguishers. We see in the previous statement in the algorithm. If Y is in W, it has just become obsolete as a means to split classes in future iterations. So Y must be replaced by both splits because of the Observation above. If Y is not in W, only one of the two splits, not both, needs to be added to W because of the Lemma above.
Choosing the smaller of the two splits guarantees that the new addition to W is no more than half the size of Y. The worst case running time of this algorithm is O, where n is the number of states and s is the size of the alphabet; this bound follows from the fact that, for each of the ns transitions of the automaton, the sets drawn from Q that contain the target state of the transition have sizes that decrease relative to each other by a factor of two or more, so each transition participates in O of the splitting steps in the algorithm. The partition refinement data structure allows each splitting step to be performed in time proportional to the number of transitions that participate in it; this remains the most efficient algorithm known for solving the problem, for certain distributions of inputs its average-case complexity is better, O. Once Hopcroft's algorithm has been used to group the states of the input DFA into equivalence classes, the minimum DFA can be constructed by forming one state for each equivalence class.
If S is a set of
A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, given any computer algorithm, a Turing machine capable of simulating that algorithm's logic can be constructed; the machine operates on an infinite memory tape divided into discrete "cells". The machine positions its "head" over a cell and "reads" or "scans" the symbol there; as per the symbol and its present place in a "finite table" of user-specified instructions, the machine writes a symbol in the cell either moves the tape one cell left or right either proceeds to a subsequent instruction or halts the computation. The Turing machine was invented in 1936 by Alan Turing, who called it an "a-machine". With this model, Turing was able to answer two questions in the negative: Does a machine exist that can determine whether any arbitrary machine on its tape is "circular", thus by providing a mathematical description of a simple device capable of arbitrary computations, he was able to prove properties of computation in general—and in particular, the uncomputability of the Entscheidungsproblem.
Thus, Turing machines prove fundamental limitations on the power of mechanical computation. While they can express arbitrary computations, their minimalistic design makes them unsuitable for computation in practice: real-world computers are based on different designs that, unlike Turing machines, use random-access memory. Turing completeness is the ability for a system of instructions to simulate a Turing machine. A programming language, Turing complete is theoretically capable of expressing all tasks accomplishable by computers. A Turing machine is a general example of a CPU that controls all data manipulation done by a computer, with the canonical machine using sequential memory to store data. More it is a machine capable of enumerating some arbitrary subset of valid strings of an alphabet. A Turing machine has a tape of infinite length on which it can perform write operations. Assuming a black box, the Turing machine cannot know whether it will enumerate any one specific string of the subset with a given program.
This is due to the fact that the halting problem is unsolvable, which has major implications for the theoretical limits of computing. The Turing machine is capable of processing an unrestricted grammar, which further implies that it is capable of robustly evaluating first-order logic in an infinite number of ways; this is famously demonstrated through lambda calculus. A Turing machine, able to simulate any other Turing machine is called a universal Turing machine. A more mathematically oriented definition with a similar "universal" nature was introduced by Alonzo Church, whose work on lambda calculus intertwined with Turing's in a formal theory of computation known as the Church–Turing thesis; the thesis states that Turing machines indeed capture the informal notion of effective methods in logic and mathematics, provide a precise definition of an algorithm or "mechanical procedure". Studying their abstract properties yields many insights into computer science and complexity theory. In his 1948 essay, "Intelligent Machinery", Turing wrote that his machine consisted of:...an unlimited memory capacity obtained in the form of an infinite tape marked out into squares, on each of which a symbol could be printed.
At any moment there is one symbol in the machine. The machine can alter the scanned symbol, its behavior is in part determined by that symbol, but the symbols on the tape elsewhere do not affect the behavior of the machine. However, the tape can be moved back and forth through the machine, this being one of the elementary operations of the machine. Any symbol on the tape may therefore have an innings; the Turing machine mathematically models a machine. On this tape are symbols, which the machine can read and write, one at a time, using a tape head. Operation is determined by a finite set of elementary instructions such as "in state 42, if the symbol seen is 0, write a 1. In the original article, Turing imagines not a mechanism, but a person whom he calls the "computer", who executes these deterministic mechanical rules slavishly. More a Turing machine consists of: A tape divided into cells, one next to the other; each cell contains a symbol from some finite alphabet. The alphabet contains one or more other symbols.
The tape is assumed to be arbitrarily extendable to the left and to the right, i.e. the Turing machine is always supplied with as much tape as it needs for its computation. Cells that have not been written before are assumed to be filled with the blank symbol. In some models the tape has a left e
A palindrome is a word, phrase, or other sequence of characters which reads the same backward as forward, such as madam or racecar or the number 10801. Sentence-length palindromes may be written when allowances are made for adjustments to capital letters and word dividers, such as "A man, a plan, a canal, Panama!", "Was it a car or a cat I saw?" or "No'x' in Nixon". Composing literature in palindromes is an example of constrained writing; the word "palindrome" was coined by the English playwright Ben Jonson in the 17th century from the Greek roots palin and dromos. Palindromes date back at least to 79 AD, as a palindrome was found as a graffito at Herculaneum, a city buried by ash in that year; this palindrome, called the Sator Square, consists of a sentence written in Latin: "Sator Arepo Tenet Opera Rotas". It is remarkable for the fact that the first letters of each word form the first word, the second letters form the second word, so forth. Hence, it can be arranged into a word square that reads in four different ways: horizontally or vertically from either top left to bottom right or bottom right to top left.
As such, they can be referred to as palindromatic. A palindrome with the same square property is the Hebrew palindrome, "We explained the glutton, in the honey was burned and incinerated", credited to Abraham ibn Ezra in 1924, referring to the halachic question as to whether a fly landing in honey makes the honey treif; the palindromic Latin riddle "In girum imus nocte et consumimur igni" describes the behavior of moths. It is that this palindrome is from medieval rather than ancient times; the second word, borrowed from Greek, should properly be spelled gyrum. Byzantine Greeks inscribed the palindrome, "Wash sins, not only face" ΝΙΨΟΝ ΑΝΟΜΗΜΑΤΑ ΜΗ ΜΟΝΑΝ ΟΨΙΝ, on baptismal fonts; this practice was continued in many English churches. Examples include the font at St. Mary's Church and the font in the basilica of St. Sophia, the font of St. Stephen d'Egres, Paris; some well-known English palindromes are, "Able was I ere I saw Elba", "A man, a plan, a canal – Panama", "Madam, I'm Adam" and "Never odd or even".
English palindromes of notable length include mathematician Peter Hilton's "Doc, note: I dissent. A fast never prevents a fatness. I diet on cod" and Scottish poet Alastair Reid's "T. Eliot, top bard, notes putrid tang emanating, is sad; the most familiar palindromes in English are character-unit palindromes. The characters read the same backward as forward; some examples of palindromic words are redivider, civic, level, kayak, racecar, redder and refer. There are word-unit palindromes in which the unit of reversal is the word. Word-unit palindromes were made popular in the recreational linguistics community by J. A. Lindon in the 1960s. Occasional examples in English were created in the 19th century. Several in French and Latin date to the Middle Ages. There are line-unit palindromes. Palindromes consist of a sentence or phrase, e.g. "Mr. Owl ate my metal worm", "Was it a car or a cat I saw?", "Murder for a jar of red rum" or "Go hang a salami, I'm a lasagna hog". Punctuation and spaces are ignored.
Some, such as "Rats live on no evil star", "Live on time, emit no evil", "Step on no pets", include the spaces. Semordnilap is a name coined for words; the word was coined by Martin Gardner in his notes to C. C. Bombaugh's book Oddities and Curiosities of Words and Literature in 1961. An example of this is the word stressed, desserts spelled backward; some semordnilaps are deliberate. An example in electronics is the mho, a unit of electrical conductance, ohm spelled backwards, the unit of electrical resistance and the reciprocal of conductance; the daraf, a unit of elastance, is farad spelled backwards, the unit of capacitance and the reciprocal of elastance. In fiction, many characters have names deliberately made to be semordnilaps of other names or words, the most used of, Alucard. Semordnilaps are known as emordnilaps, word reversals, reversible anagrams, heteropalindromes, semi-palindromes, half-palindromes, mynoretehs, volvograms, or anadromes, they have sometimes been called antigrams, though this term refers to anagrams which have opposite meanings.
In 2017, a six-year-old Canadian named Levi Budd called this a levidrome, which garnered support into making it a word from celebrities William Shatner and Patricia Arquette As of October 2018, none of these terms have been accepted as official entries in the Oxford English Dictionary. Some names are palindromes, such as the given names Hannah, Anna, Bob and Otto, or the surnames Harrah, Renner and Nenonen. Lon Nol was Prime Minister of Cambodia. Nisio Isin is a Japanese novelist and manga writer, whose pseudonym is a pal
A finite-state machine or finite-state automaton, finite automaton, or a state machine, is a mathematical model of computation. It is an abstract machine that can be in one of a finite number of states at any given time; the FSM can change from one state to another in response to some external inputs. An FSM is defined by a list of its states, its initial state, the conditions for each transition. Finite state machines are of two types – deterministic finite state machines and non-deterministic finite state machines. A deterministic finite-state machine can be constructed equivalent to any non-deterministic one; the behavior of state machines can be observed in many devices in modern society that perform a predetermined sequence of actions depending on a sequence of events with which they are presented. Simple examples are vending machines, which dispense products when the proper combination of coins is deposited, whose sequence of stops is determined by the floors requested by riders, traffic lights, which change sequence when cars are waiting, combination locks, which require the input of combination numbers in the proper order.
The finite state machine has less computational power than some other models of computation such as the Turing machine. The computational power distinction means there are computational tasks that a Turing machine can do but a FSM cannot; this is because a FSM's memory is limited by the number of states it has. FSMs are studied in the more general field of automata theory. An example of a simple mechanism that can be modeled by a state machine is a turnstile. A turnstile, used to control access to subways and amusement park rides, is a gate with three rotating arms at waist height, one across the entryway; the arms are locked, blocking the entry, preventing patrons from passing through. Depositing a coin or token in a slot on the turnstile unlocks the arms, allowing a single customer to push through. After the customer passes through, the arms are locked again. Considered as a state machine, the turnstile has two possible states: Unlocked. There are two possible inputs that affect its state: pushing the arm.
In the locked state, pushing on the arm has no effect. Putting a coin in – that is, giving the machine a coin input – shifts the state from Locked to Unlocked. In the unlocked state, putting additional coins in has no effect. However, a customer pushing through the arms, giving a push input, shifts the state back to Locked; the turnstile state machine can be represented by a state transition table, showing for each possible state, the transitions between them and the outputs resulting from each input: The turnstile state machine can be represented by a directed graph called a state diagram. Each state is represented by a node. Edges show the transitions from one state to another; each arrow is labeled with the input. An input that doesn't cause a change of state is represented by a circular arrow returning to the original state; the arrow into the Locked node from the black dot indicates. A state is a description of the status of a system, waiting to execute a transition. A transition is a set of actions to be executed when a condition is fulfilled or when an event is received.
For example, when using an audio system to listen to the radio, receiving a "next" stimulus results in moving to the next station. When the system is in the "CD" state, the "next" stimulus results in moving to the next track. Identical stimuli trigger different actions depending on the current state. In some finite-state machine representations, it is possible to associate actions with a state: an entry action: performed when entering the state, an exit action: performed when exiting the state. Several state transition table types are used; the most common representation is shown below: the combination of current state and input shows the next state. The complete action's information is not directly described in the table and can only be added using footnotes. A FSM definition including the full actions information is possible using state tables; the Unified Modeling Language has a notation for describing state machines. UML state machines overcome the limitations of traditional finite state machines while retaining their main benefits.
UML state machines introduce the new concepts of hierarchically nested states and orthogonal regions, while extending the notion of actions. UML state machines have the characteristics of Moore machines, they support actions that depend on both the state of the system and the triggering event, as in Mealy machines, as well as entry and exit actions, which are associated with states rather than transitions, as in Moore machines. The Specification and Description Language is a standard from ITU that includes graphical symbols to describe actions in the transition: send an event receive an event start a timer cancel a timer start another concurrent state machine decisionSDL embeds basic data types called "Abstract Data Types", an action language, an execution semantic in order to make the finite state machine executable. There are a large number of variants to represent an FSM such as the one in figure 3. In addition to their use in modeling reactive systems