Finitestate machine
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A finitestate machine (FSM) or finitestate automaton (plural: automata), or simply a state machine, is a mathematical model of computation used to design both computer programs and sequential logic circuits. It is conceived as an abstract machine that can be in one of a finite number of states. The machine is in only one state at a time; the state it is in at any given time is called the current state. It can change from one state to another when initiated by a triggering event or condition; this is called a transition. A particular FSM is defined by a list of its states, and the triggering condition for each transition.
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, elevators, which drop riders off at upper floors before going down, traffic lights, which change sequence when cars are waiting, and combination locks, which require the input of combination numbers in the proper order.
Finitestate machines can model a large number of problems, among which are electronic design automation, communication protocol design, language parsing and other engineering applications. In biology and artificial intelligence research, state machines or hierarchies of state machines have been used to describe neurological systems. In linguistics, they are used to describe simple parts of the grammars of natural languages.
Considered as an abstract model of computation, the finite state machine is weak; it has less computational power than some other models of computation such as the Turing machine.^{[1]} That is, there are tasks that no FSM can do, but some Turing machines can. This is because the FSM memory is limited by the number of states.
FSMs are studied in the more general field of automata theory.
Contents
 1 Example: coinoperated turnstile
 2 Concepts and terminology
 3 Representations
 4 Usage
 5 Classification
 6 Alternative semantics
 7 FSM logic
 8 Mathematical model
 9 Optimization
 10 Implementation
 11 See also
 12 References
 13 Further reading
 13.1 General
 13.2 Finite state machines (automata theory) in theoretical computer science
 13.3 Abstract state machines in theoretical computer science
 13.4 Machine learning using finitestate algorithms
 13.5 Hardware engineering: state minimization and synthesis of sequential circuits
 13.6 Finite Markov chain processes
 14 External links
Example: coinoperated turnstile
An example of a very simple mechanism that can be modeled by a state machine is a turnstile.^{[2]}^{[3]} 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. Initially 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 until another coin is inserted.
Considered as a state machine, the turnstile has two states: Locked and Unlocked.^{[2]} There are two inputs that affect its state: putting a coin in the slot (coin) and pushing the arm (push). In the locked state, pushing on the arm has no effect; no matter how many times the input push is given, it stays in the locked state. 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; that is, giving additional coin inputs does not change the state. 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 state the new state and the output (action) resulting from each input
Current State  Input  Next State  Output 

Locked  coin  Unlocked  Unlock turnstile so customer can push through 
push  Locked  None  
Unlocked  coin  Unlocked  None 
push  Locked  When customer has pushed through, lock turnstile 
It can also be represented by a directed graph called a state diagram (above). Each of the states is represented by a node (circle). Edges (arrows) show the transitions from one state to another. Each arrow is labeled with the input that triggers that transition. Inputs that don't cause a change of state (such as a coin input in the Unlocked state) are represented by a circular arrow returning to the original state. The arrow into the Locked node from the black dot indicates it is the initial state.
Concepts and terminology
A state is a description of the status of a system that is 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 (the system is in the "radio" state), 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 finitestate machine representations, it is also possible to associate actions with a state:
 Entry action: performed when entering the state,
 Exit action: performed when exiting the state.
Representations
State/Event table
Several state transition table types are used. The most common representation is shown below: the combination of current state (e.g. B) and input (e.g. Y) shows the next state (e.g. C). 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 (see also virtual finitestate machine).
Current state
Input

State A  State B  State C 

Input X  …  …  … 
Input Y  …  State C  … 
Input Z  …  …  … 
UML state machines
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 both Mealy machines and 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.
SDL state 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
 decision
SDL embeds basic data types called Abstract Data Types, an action language, and an execution semantic in order to make the finite state machine executable.
Other state diagrams
There are a large number of variants to represent an FSM such as the one in figure 3.
Usage
In addition to their use in modeling reactive systems presented here, finite state automata are significant in many different areas, including electrical engineering, linguistics, computer science, philosophy, biology, mathematics, and logic. Finite state machines are a class of automata studied in automata theory and the theory of computation. In computer science, finite state machines are widely used in modeling of application behavior, design of hardware digital systems, software engineering, compilers, network protocols, and the study of computation and languages.
Classification
The state machines can be subdivided into Transducers, Acceptors, Classifiers and Sequencers.^{[4]}
Acceptors and recognizers
Acceptors (also recognizers and sequence detectors) produce a binary output, saying either yes or no to answer whether the input is accepted by the machine or not. All states of the FSM are said to be either accepting or not accepting. At the time when all input is processed, if the current state is an accepting state, the input is accepted; otherwise it is rejected. As a rule the input are symbols (characters); actions are not used. The example in figure 4 shows a finite state machine that accepts the string "nice". In this FSM the only accepting state is number 7.
The machine can also be described as defining a language, which would contain every string accepted by the machine but none of the rejected ones; we say then that the language is accepted by the machine. By definition, the languages accepted by FSMs are the regular languages—that is, a language is regular if there is some FSM that accepts it.
The problem of determining the language accepted by a given FSA is an instance of the algebraic path problem—itself a generalization of the shortest path problem to graphs with edges weighted by the elements of an (arbitrary) semiring.^{[5]}^{[6]}^{[7]}
Start state
The start state is usually shown drawn with an arrow "pointing at it from any where" (Sipser (2006) p. 34).
Accept (or final) states
Accept states (also referred to as accepting or final states) are those at which the machine reports that the input string, as processed so far, is a member of the language it accepts. It is usually represented by a double circle.
The start state can also be an accepting state, in which case the automaton accepts the empty string. If the start state is not an accepting state and there are no connecting edges to any of the accepting states, then the automaton is accepting nothing.
An example of an accepting state appears in Fig.5: a deterministic finite automaton (DFA) that detects whether the binary input string contains an even number of 0s.
S_{1} (which is also the start state) indicates the state at which an even number of 0s has been input. S_{1} is therefore an accepting state. This machine will finish in an accept state, if the binary string contains an even number of 0s (including any binary string containing no 0s). Examples of strings accepted by this DFA are ε (the empty string), 1, 11, 11…, 00, 010, 1010, 10110, etc…
Classifier is a generalization that, similar to acceptor, produces single output when terminates but has more than two terminal states.
Transducers
Transducers generate output based on a given input and/or a state using actions. They are used for control applications and in the field of computational linguistics.
In control applications, two types are distinguished:
 Moore machine
 The FSM uses only entry actions, i.e., output depends only on the state. The advantage of the Moore model is a simplification of the behaviour. Consider an elevator door. The state machine recognizes two commands: "command_open" and "command_close", which trigger state changes. The entry action (E:) in state "Opening" starts a motor opening the door, the entry action in state "Closing" starts a motor in the other direction closing the door. States "Opened" and "Closed" stop the motor when fully opened or closed. They signal to the outside world (e.g., to other state machines) the situation: "door is open" or "door is closed".
 Mealy machine
 The FSM uses only input actions, i.e., output depends on input and state. The use of a Mealy FSM leads often to a reduction of the number of states. The example in figure 7 shows a Mealy FSM implementing the same behaviour as in the Moore example (the behaviour depends on the implemented FSM execution model and will work, e.g., for virtual FSM but not for event driven FSM). There are two input actions (I:): "start motor to close the door if command_close arrives" and "start motor in the other direction to open the door if command_open arrives". The "opening" and "closing" intermediate states are not shown.
Generators
The sequencers or generators are a subclass of aforementioned types that have a singleletter input alphabet. They produce only one sequence, which can be interpreted as output sequence of transducer or classifier outputs.
Determinism
A further distinction is between deterministic (DFA) and nondeterministic (NFA, GNFA) automata. In deterministic automata, every state has exactly one transition for each possible input. In nondeterministic automata, an input can lead to one, more than one or no transition for a given state. This distinction is relevant in practice, but not in theory, as there exists an algorithm (the powerset construction) that can transform any NFA into a more complex DFA with identical functionality.
The FSM with only one state is called a combinatorial FSM and uses only input actions. This concept is useful in cases where a number of FSM are required to work together, and where it is convenient to consider a purely combinatorial part as a form of FSM to suit the design tools.^{[8]}
Alternative semantics
There are other sets of semantics available to represent state machines. For example, there are tools for modeling and designing logic for embedded controllers.^{[9]} They combine hierarchical state machines, flow graphs, and truth tables into one language, resulting in a different formalism and set of semantics.^{[10]} Figure 8 illustrates this mix of state machines and flow graphs with a set of states to represent the state of a stopwatch and a flow graph to control the ticks of the watch. These charts, like Harel's original state machines,^{[11]} support hierarchically nested states, orthogonal regions, state actions, and transition actions.^{[12]}
FSM logic
The next state and output of an FSM is a function of the input and of the current state. The FSM logic is shown in Figure 8.
Mathematical model
In accordance with the general classification, the following formal definitions are found:
 A deterministic finite state machine or acceptor deterministic finite state machine is a quintuple , where:
 is the input alphabet (a finite, nonempty set of symbols).
 is a finite, nonempty set of states.
 is an initial state, an element of .
 is the statetransition function: (in a nondeterministic finite automaton it would be , i.e., would return a set of states).
 is the set of final states, a (possibly empty) subset of .
For both deterministic and nondeterministic FSMs, it is conventional to allow to be a partial function, i.e. does not have to be defined for every combination of and . If an FSM is in a state , the next symbol is and is not defined, then can announce an error (i.e. reject the input). This is useful in definitions of general state machines, but less useful when transforming the machine. Some algorithms in their default form may require total functions.
A finitestate machine is a restricted Turing machine where the head can only perform "read" operations, and always moves from left to right.^{[13]}
 A finite state transducer is a sextuple , where:
 is the input alphabet (a finite nonempty set of symbols).
 is the output alphabet (a finite, nonempty set of symbols).
 is a finite, nonempty set of states.
 is the initial state, an element of . In a nondeterministic finite automaton, is a set of initial states.
 is the statetransition function: .
 is the output function.
If the output function is a function of a state and input alphabet () that definition corresponds to the Mealy model, and can be modelled as a Mealy machine. If the output function depends only on a state () that definition corresponds to the Moore model, and can be modelled as a Moore machine. A finitestate machine with no output function at all is known as a semiautomaton or transition system.
If we disregard the first output symbol of a Moore machine, , then it can be readily converted to an outputequivalent Mealy machine by setting the output function of every Mealy transition (i.e. labeling every edge) with the output symbol given of the destination Moore state. The converse transformation is less straightforward because a Mealy machine state may have different output labels on its incoming transitions (edges). Every such state needs to be split in multiple Moore machine states, one for every incident output symbol.^{[14]}
Optimization
Optimizing an FSM means finding the machine with the minimum number of states that performs the same function. The fastest known algorithm doing this is the Hopcroft minimization algorithm.^{[15]}^{[16]} Other techniques include using an implication table, or the Moore reduction procedure. Additionally, acyclic FSAs can be minimized in linear time.^{[17]}
Implementation
Hardware applications
In a digital circuit, an FSM may be built using a programmable logic device, a programmable logic controller, logic gates and flip flops or relays. More specifically, a hardware implementation requires a register to store state variables, a block of combinational logic that determines the state transition, and a second block of combinational logic that determines the output of an FSM. One of the classic hardware implementations is the Richards controller.
A particular case of Moore FSM, when output is directly connected to the state flipflops, that is when output function is simple identity, is known as Medvedev FSM.^{[18]} It is advised in chip design that no logic is placed between primary I/O and registers to minimize interchip delays, which are usually long and limit the FSM frequencies.
Through state encoding for low power state machines may be optimized to minimize power consumption.
Software applications
The following concepts are commonly used to build software applications with finite state machines:
Finite automata and compilers
Finite automata are often used in the frontend of programming language compilers. Such a frontend may comprise several finite state machines that implement a lexical analyzer and a parser. Starting from a sequence of characters, the lexical analyzer builds a sequence of language tokens (such as reserved words, literals, and identifiers) from which the parser builds a syntax tree. The lexical analyzer and the parser handle the regular and contextfree parts of the programming language's grammar.^{[19]}
See also
 Abstract state machines (ASM)
 Artificial intelligence (AI)
 Abstract State Machine Language (AsmL)
 Behavior model
 Communicating finitestate machine
 Control system
 Control table
 Decision tables
 DEVS: Discrete Event System Specification
 Extended finitestate machine (EFSM)
 Finite state machine with datapath
 Hidden Markov model
 Petri net
 Pushdown automaton
 Quantum finite automata (QFA)
 Recognizable language
 Sequential logic
 Specification and Description Language
 State diagram
 SCXML
 Transition system
 Tree automaton
 Turing machine
 UML state machine
 YAKINDU Statechart Tools
References
 ↑ Belzer, Jack; Holzman, Albert George; Kent, Allen (1975). Encyclopedia of Computer Science and Technology, Vol. 25. USA: CRC Press. p. 73. ISBN 0824722752.
 ↑ ^{2.0} ^{2.1} Koshy, Thomas (2004). Discrete Mathematics With Applications. Academic Press. p. 762. ISBN 0124211801.
 ↑ Wright, David R. (2005). "Finite State Machines" (PDF). CSC215 Class Notes. Prof. David R. Wright website, N. Carolina State Univ. Retrieved July 14, 2012.
 ↑ Keller, Robert M. (2001). "Classifiers, Acceptors, Transducers, and Sequencers" (PDF). Computer Science: Abstraction to Implementation (PDF). Harvey Mudd College. p. 480.
 ↑ Pouly, Marc; Kohlas, Jürg (2011). Generic Inference: A Unifying Theory for Automated Reasoning. John Wiley & Sons. Chapter 6. Valuation Algebras for Path Problems, p. 223 in particular. ISBN 9781118010860.
 ↑ Storer, J. A. (2001). An Introduction to Data Structures and Algorithms. Springer Science & Business Media. p. 337. ISBN 9780817642532.
 ↑ http://www.iam.unibe.ch/~run/talks/20080605BernJonczy.pdf, p. 34
 ↑ Brutscheck, M., Berger, S., Franke, M., Schwarzbacher, A., Becker, S.: Structural Division Procedure for Efficient IC Analysis. IET Irish Signals and Systems Conference, (ISSC 2008), pp.1823. Galway, Ireland, 18–19 June 2008. [1]
 ↑ Tiwari, A. (2002). Formal Semantics and Analysis Methods for Simulink Stateflow Models.
 ↑ Hamon, G. (2005). A Denotational Semantics for Stateflow. International Conference on Embedded Software. Jersey City, NJ: ACM. pp. 164–172. CiteSeerX: 10.1.1.89.8817.
 ↑ Harel, D. (1987). A Visual Formalism for Complex Systems. Science of Computer Programming , 231–274.
 ↑ Alur, R., Kanade, A., Ramesh, S., & Shashidhar, K. C. (2008). Symbolic analysis for improving simulation coverage of Simulink/Stateflow models. International Conference on Embedded Software (pp. 89–98). Atlanta, GA: ACM.
 ↑ Black, Paul E (12 May 2008). "Finite State Machine". Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology.
 ↑ Anderson, James Andrew; Head, Thomas J. (2006). Automata theory with modern applications. Cambridge University Press. pp. 105–108. ISBN 9780521848879.
 ↑ Hopcroft, John E. (1971). An n log n algorithm for minimizing states in a finite automaton (PDF) (Technical Report). CSTR71190. Stanford Univ.
 ↑ Almeida, Marco; Moreira, Nelma; Reis, Rogerio (2007). On the performance of automata minimization algorithms (PDF) (Technical Report). DCC200703. Porto Univ.
 ↑ Revuz, D. (1992). "Minimization of Acyclic automata in Linear Time". Theoretical Computer Science. Elsevier. 92: 181–189. doi:10.1016/03043975(92)901423.
 ↑ Kaeslin, Hubert (2008). "Mealy, Moore, Medvedevtype and combinatorial output bits". Digital Integrated Circuit Design: From VLSI Architectures to CMOS Fabrication. Cambridge University Press. p. 787. ISBN 9780521882675.
 ↑ Aho, Alfred V.; Sethi, Ravi; Ullman, Jeffrey D. (1986). Compilers: Principles, Techniques, and Tools (1st ed.). AddisonWesley. ISBN 9780201100884.
Further reading
General
 Sakarovitch, Jacques (2009). Elements of automata theory. Translated from the French by Reuben Thomas. Cambridge University Press. ISBN 9780521844253. Zbl 1188.68177
 Wagner, F., "Modeling Software with Finite State Machines: A Practical Approach", Auerbach Publications, 2006, ISBN 0849380863.
 ITUT, Recommendation Z.100 Specification and Description Language (SDL)
 Samek, M., Practical Statecharts in C/C++, CMP Books, 2002, ISBN 1578201101.
 Samek, M., Practical UML Statecharts in C/C++, 2nd Edition, Newnes, 2008, ISBN 0750687061.
 Gardner, T., Advanced State Management, 2007
 Cassandras, C., Lafortune, S., "Introduction to Discrete Event Systems". Kluwer, 1999, ISBN 0792386094.
 Timothy Kam, Synthesis of Finite State Machines: Functional Optimization. Kluwer Academic Publishers, Boston 1997, ISBN 0792398424
 Tiziano Villa, Synthesis of Finite State Machines: Logic Optimization. Kluwer Academic Publishers, Boston 1997, ISBN 0792398920
 Carroll, J., Long, D., Theory of Finite Automata with an Introduction to Formal Languages. Prentice Hall, Englewood Cliffs, 1989.
 Kohavi, Z., Switching and Finite Automata Theory. McGrawHill, 1978.
 Gill, A., Introduction to the Theory of Finitestate Machines. McGrawHill, 1962.
 Ginsburg, S., An Introduction to Mathematical Machine Theory. AddisonWesley, 1962.
Finite state machines (automata theory) in theoretical computer science
 Arbib, Michael A. (1969). Theories of Abstract Automata (1st ed.). Englewood Cliffs, N.J.: PrenticeHall, Inc. ISBN 0139133682.
 Bobrow, Leonard S.; Arbib, Michael A. (1974). Discrete Mathematics: Applied Algebra for Computer and Information Science (1st ed.). Philadelphia: W. B. Saunders Company, Inc. ISBN 0721617689.
 Booth, Taylor L. (1967). Sequential Machines and Automata Theory (1st ed.). New York: John Wiley and Sons, Inc. Library of Congress Card Catalog Number 6725924.
 Boolos, George; Jeffrey, Richard (1999) [1989]. Computability and Logic (3rd ed.). Cambridge, England: Cambridge University Press. ISBN 052120402X.
 Brookshear, J. Glenn (1989). Theory of Computation: Formal Languages, Automata, and Complexity. Redwood City, California: Benjamin/Cummings Publish Company, Inc. ISBN 0805301437.
 Davis, Martin; Sigal, Ron; Weyuker, Elaine J. (1994). Computability, Complexity, and Languages and Logic: Fundamentals of Theoretical Computer Science (2nd ed.). San Diego: Academic Press, Harcourt, Brace & Company. ISBN 0122063821.
 Hopcroft, John; Ullman, Jeffrey (1979). Introduction to Automata Theory, Languages, and Computation (1st ed.). Reading Mass: AddisonWesley. ISBN 020102988X.
 Hopcroft, John E.; Motwani, Rajeev; Ullman, Jeffrey D. (2001). Introduction to Automata Theory, Languages, and Computation (2nd ed.). Reading Mass: AddisonWesley. ISBN 0201441241.
 Hopkin, David; Moss, Barbara (1976). Automata. New York: Elsevier NorthHolland. ISBN 0444002499.
 Kozen, Dexter C. (1997). Automata and Computability (1st ed.). New York: SpringerVerlag. ISBN 0387949070.
 Lewis, Harry R.; Papadimitriou, Christos H. (1998). Elements of the Theory of Computation (2nd ed.). Upper Saddle River, New Jersey: PrenticeHall. ISBN 0132624788.
 Linz, Peter (2006). Formal Languages and Automata (4th ed.). Sudbury, MA: Jones and Bartlett. ISBN 9780763737986.
 Minsky, Marvin (1967). Computation: Finite and Infinite Machines (1st ed.). New Jersey: PrenticeHall.
 Papadimitriou, Christos (1993). Computational Complexity (1st ed.). Addison Wesley. ISBN 0201530821.
 Pippenger, Nicholas (1997). Theories of Computability (1st ed.). Cambridge, England: Cambridge University Press. ISBN 0521553806.
 Rodger, Susan; Finley, Thomas (2006). JFLAP: An Interactive Formal Languages and Automata Package (1st ed.). Sudbury, MA: Jones and Bartlett. ISBN 0763738344.
 Sipser, Michael (2006). Introduction to the Theory of Computation (2nd ed.). Boston Mass: Thomson Course Technology. ISBN 0534950973.
 Wood, Derick (1987). Theory of Computation (1st ed.). New York: Harper & Row, Publishers, Inc. ISBN 0060472081.
Abstract state machines in theoretical computer science
 Gurevich, Yuri (July 2000). "Sequential Abstract State Machines Capture Sequential Algorithms" (PDF). ACM Transactions on Computational Logic. 1 (1): 77–111. doi:10.1145/343369.343384.
Machine learning using finitestate algorithms
 Mitchell, Tom M. (1997). Machine Learning (1st ed.). New York: WCB/McGrawHill Corporation. ISBN 0070428077.
Hardware engineering: state minimization and synthesis of sequential circuits
 Booth, Taylor L. (1967). Sequential Machines and Automata Theory (1st ed.). New York: John Wiley and Sons, Inc. Library of Congress Card Catalog Number 6725924.
 Booth, Taylor L. (1971). Digital Networks and Computer Systems (1st ed.). New York: John Wiley and Sons, Inc. ISBN 0471088404.
 McCluskey, E. J. (1965). Introduction to the Theory of Switching Circuits (1st ed.). New York: McGrawHill Book Company, Inc. Library of Congress Card Catalog Number 6517394.
 Hill, Fredrick J.; Peterson, Gerald R. (1965). Introduction to the Theory of Switching Circuits (1st ed.). New York: McGrawHill Book Company. Library of Congress Card Catalog Number 6517394.
Finite Markov chain processes

 "We may think of a Markov chain as a process that moves successively through a set of states s_{1}, s_{2}, …, s_{r}. … if it is in state s_{i} it moves on to the next stop to state s_{j} with probability p_{ij}. These probabilities can be exhibited in the form of a transition matrix" (Kemeny (1959), p. 384)
Finite Markovchain processes are also known as subshifts of finite type.
 Booth, Taylor L. (1967). Sequential Machines and Automata Theory (1st ed.). New York: John Wiley and Sons, Inc. Library of Congress Card Catalog Number 6725924.
 Kemeny, John G.; Mirkil, Hazleton; Snell, J. Laurie; Thompson, Gerald L. (1959). Finite Mathematical Structures (1st ed.). Englewood Cliffs, N.J.: PrenticeHall, Inc. Library of Congress Card Catalog Number 5912841. Chapter 6 "Finite Markov Chains".
External links
Wikimedia Commons has media related to Finite state machine. 
 Finite State Automata at DMOZ
 Modeling a Simple AI behavior using a Finite State Machine Example of usage in Video Games
 Free OnLine Dictionary of Computing description of Finite State Machines
 NIST Dictionary of Algorithms and Data Structures description of Finite State Machines
 Interactive FSM: Control Circuit, demonstrates the logic flow of the Finite State Machines.
 FSM simulator, simulates DFAs, NFAs and εNFAs, including generated by regular expression.