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A finite state machine (FSM) or '''finite state automaton''' (plural: ''automata'') or simply a '''state machine''' is a model of behavior composed of a finite number of State s, transitions between those states, and actions.


CONCEPTS AND VOCABULARY

A state stores information about the past, i.e. it reflects the input changes from the system start to the present moment. A transition indicates a state change and is described by a condition that would need to be fulfilled to enable the transition. An action is a description of an activity that is to be performed at a given moment. There are several action types:
;Entry action: which is performed ''when entering'' the state
;Exit action: which is performed ''when exiting'' the state
;Input action: which is performed depending on present state and input conditions
;Transition action: which is performed when performing a certain transition

A FSM can be represented using a (see also VFSM ).

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 , Mathematic s, and Logic . A complete survey of their applications is outside the scope of this article. 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

There are two different groups: Acceptors/Recognizers and Transducers.


Acceptors and recognizers


Acceptors and '''recognizers''' (also '''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 2 shows a finite state machine which accepts the word "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 word 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 Language s - that is, a language is regular if there is some FSM that accepts it (cf. Kleene's Theorem).


Start state

The start state is usually shown drawn with an arrow "pointing at it from nowhere" (Sipser (2006) p.34)


Accept state

An accept state (sometimes referred to as an '''accepting state''') is a state at which the machine has successfully performed its procedure. It is usually represented by a double circle.

An example of an accepting state appears on the left in this diagram of a Deterministic Finite Automaton which determines if the Binary input 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 and is therefore defined as an accepting state.


Transducers

Transducers generate output based on a given input and/or a state using actions. They are used for control applications. Here 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. The example in figure 3 shows a Moore FSM of 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" don't perform any actions. They signal to the outside world (e.g. to other state machines) the situation: "door is open" or "door is closed".

; 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".

In practice mixed models are often used.

More details about the differences and usage of Moore and Mealy models, including an executable example, can be found in the external technical note "Moore or Mealy model?"

A further distinction is between deterministic ( DFA ) and '''non-deterministic''' ( NDFA , GNFA ) automata. In deterministic automata, for each state there is exactly one transition for each possible input. In non-deterministic automata, there can be none or more than one transition from a given state for a given possible input. This distinction is relevant in practice, but not in theory, as there exists an algorithm which can transform any NDFA into an equivalent DFA, although this transformation typically significantly increases the complexity of the automaton.

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.


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 5.


MATHEMATICAL MODEL

Depending on the type there are several definitions. An acceptor finite state machine is a Quintuple (\Sigma, S, s_0, \delta, F), where:
  • \Sigma is the input Alphabet (a finite non empty set of symbols).

  • S is a finite non empty set of states.

  • s_0 is an initial state, an element of S. In a Nondeterministic Finite State Machine , s_0 is a set of initial states.

  • \delta is the state transition function: \delta: S imes \Sigma ightarrow S.

  • F is the set of final states, a (possibly empty) subset of S.


A transducer finite state machine is a six tuple (\Sigma, \Gamma, S, s_0, \delta, \omega), where:
  • \Sigma is the input Alphabet (a finite non empty set of symbols).

  • \Gamma is the output alphabet (a finite non empty set of symbols).

  • S is a finite non empty set of states.

  • s_0 is the initial state, an element of S. In a Nondeterministic Finite State Machine , s_0 is a set of initial states.

  • \delta is the state transition function: \delta: S imes \Sigma ightarrow S.

  • \omega is the output function.


If the output function is a function of a state and input alphabet (\omega: S imes \Sigma ightarrow \Gamma) that definition corresponds to the Mealy model, and can be modelled as a . A finite state machine with no output function at all is known as a Semiautomaton or Transition System .


OPTIMIZATION

Optimizing an FSM means finding the machine with the minimum number of states that performs the same function. One possibility is by using an Implication Table or the Moore Reduction Procedure . Another possibility is bottom-up algorithm for Acyclic FSAs .


IMPLEMENTATION


Hardware applications

for a 4 bit TTL counter, a type of state machine]]In a Digital Circuit , an FSM may be built using a Programmable Logic Device , a Programmable Logic Controller , Logic Gate s and Flip Flop s or Relay s. More specifically, a hardware implementation requires a Register to store state variables, a block of combinational logic which 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 Richard's Controller


Software applications

The following concepts are commonly used to build software applications with finite state machines:


HISTORY


Starting in the 1970s, Leslie Lamport , an early leader within the distributed systems research community, used finite state machines as the basis for an algorithm he called State Machine Replication . In this approach, a deterministic computer program or service is replaced with a set of replicas that use some form of atomic broadcast to perform operations in a manner tolerant of failures.


SEE ALSO




EXTERNAL LINKS



REFERENCES



General

  • Wagner, F., "Modeling Software with Finite State Machines: A Practical Approach", Auerbach Publications, 2006, ISBN 0-8493-8086-3.

  • Samek, M., "Practical Statecharts in C/C++", CMP Books, 2002, ISBN 1-57820-110-1.

  • Cassandras, C., Lafortune, S., "Introduction to Discrete Event Systems". Kluwer, 1999, ISBN 0-7923-8609-4.

  • Timothy Kam, ''Synthesis of Finite State Machines: Functional Optimization''. Kluwer Academic Publishers, Boston 1997, ISBN 0-7923-9842-4

  • Tiziano Villa, ''Synthesis of Finite State Machines: Logic Optimization''. Kluwer Academic Publishers, Boston 1997, ISBN 0-7923-9892-0

  • 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''. McGraw-Hill, 1978.

  • Gill, A., ''Introduction to the Theory of Finite-state Machines''. McGraw-Hill, 1962.

  • Ginsburg, S., ''An Introduction to Mathematical Machine Theory''. Addison-Wesley, 1962.



Finite state machines (automata theory) in theoretical computer science




  • 3 Extensive, wide-ranging book meant for specialists, written for both theoretical computer scientists as well as electrical engineers. With detailed explanations of state minimization techniques, FSMs, Turing machines, Markov processes, and undecidability. Excellent treatment of Markov processes.


  • 4 Excellent. Has been in print in various editions and reprints since 1974 (1974, 1980, 1989, 1999).


  • 5 Approaches Church-Turing Thesis from three angles: levels of finite automata as acceptors of formal languages, primitive and partial recursive theory, and power of bare-bones programming languages to implement algorithms, all in one slim volume.



  • 7 A difficult book centered around the issues of machine-interpretation of "languages", NP-Completeness, etc.


  • 8 Distinctly different and less intimidating than the first edition.






  • 13 Minsky spends pages 11-20 defining what a “state” is in context of FSMs. His state diagram convention is unconventional. Excellent, i.e. relatively readable, sometimes funny.



  • 15 Abstract algebra is at the core of the book, rendering it advanced and less accessible than other texts.



  • 17 cf Finite state machines (finite automata) in chapter 29.




Abstract state machines in theoretical computer science


  • Yuri Gurevich (2000), ''Sequential Abstract State Machines Capture Sequential Algorithms'', ACM Transactions on Computational Logic, vl. 1, no. 1 (July 2000), pages 77-111. http://research.microsoft.com/~gurevich/Opera/141.pdf



Machine learning using finite-state algorithms


  • 19 A broad brush but quite thorough and sometimes difficult, meant for computer scientists and engineers. Chapter 13 ''Reinforcement Learning'' deals with robot-learning involving state-machine-like algorithms.



Hardware engineering: state minimization and synthesis of sequential circuits


  • 20 Extensive, wide-ranging book meant for specialists, written for both theoretical computer scientists as well as electrical engineers. With detailed explanations of state minimization techniques, FSMs, Turing machines, Markov processes, and undecidability. Excellent treatment of Markov processes.

  • 21 Meant for electrical engineers. More focused, less demanding than his earlier book. His treatment of computers is out-dated. Interesting take on definition of ‘algorithm’.

  • 22 Meant for hardware electrical engineers. With detailed explanations of state minimization techniques and synthesis techniques for design of combinatory logic circuits.

  • 23 Meant for hardware electrical engineers. Excellent explanations of state minimization techniques and synthesis techniques for design of combinatory and sequential logic circuits.



Finite Markov chain processes

::"We may think of a Markov Chain as a process that moves successively through a set of states s1, s2, ..., sr. ... if it is in state si it moves on to the next stop to state sj with probability pij. These probabilities can be exhibited in the form of a transition matrix" (Kemeny (1959), p. 384)
Finite Markov-chain processes are also known as Subshifts Of Finite Type .

  • 24 Extensive, wide-ranging book meant for specialists, written for both theoretical computer scientists as well as electrical engineers. With detailed explanations of state minimization techniques, FSMs, Turing machines, Markov processes, and undecidability. Excellent treatment of Markov processes.

  • 25 Classical text . cf Chapter 6 ‘’Finite Markov Chains”.