Finite state machines

A finite state machine (FSM) (or finite state automaton) is an abstract computing device consisting of:

    a set of states
    some of which are distinguished as start states
    some of which are distinguished as end states
    a set of labelled transitions between states.

We can draw an FSM as a network of nodes (representing states) joined by arrows (representing moves allowed from one state to the next), like this or this.

A finite state machine accepts a string if it is possible to trace a path from a start state to an end state, reading off the labels on the transitions as they correspond to successive symbols in the string. FSMs can thus be used as a pattern matching technique.

Alternatively, it is possible to generate the set of strings acceptable to an FSM by writing the transition labels in succession.

A simple implementation of the example is given in

In this Prolog code, strings are represented as lists of symbols e.g. [s,p,r,i,n,t]. A transition from state n to state m accepts the letter "s", for example, if the portion of the string remaining to be analyzed at state n is [s | Rest of string] and the remainder of the string at state m is just "Rest of string". An end state is said to be accepting if and only if none of the string remains to be analyzed i.e. Rest of string = [].

Running a Prolog program

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Start Prolog

At the prolog prompt ?- type


To see if the string [s,t,r,i,n,g] is acceptable, type


To nondeterministically generate an acceptable string, type


After the first answer is generated, additional solutions may be generated by typing a semicolon. To generate all of the acceptable strings, type


(That's not a built-in Prolog function, but one defined in the program.)

Symbol state table for FSM1.


Although there are no examples in FSM1, a transition may map a state onto itself i.e. the state is not changed even though the input symbol is acceptable. A "searcher" is an FSM with just two states, like this. The machine stays in the first state if any symbol other than a particular search symbol is read. If the search symbol is found however, a transition to the second state occurs. The machine stays in the second state whatever other symbols are read in. The second state is final, so if the string ends at any point after the search symbol has occurred, the input is acceptable. If, however, the search symbol does not occur in the input string, the end state will never be reached and so the input string cannot be accepted.

Deterministic and non-deterministic FSMs

From a given state, and with a given remaining string, if there is only one possible transition that can be taken, the machine is deterministic. FSM1 is non-deterministic because from some states there is a choice about which transition to follow. Computers cannot make choices, so faced with this circumstance they can only explore every possible option in turn. (That's what happens during the execution of the "loop" command.) Deterministic FSMs are sometimes more efficient than non-deterministic ones.

Finite state transducers

Transitions may be labelled with pairs of symbols, not just one symbol. A finite state machine of this kind is called a finite state transducer, and works with two strings at a time. A transition is acceptable if one element of the label is the first symbol of one string and the other element of the label is the first symbol of the other string. In this way, correspondences between the symbols of one string and symbols of the other string can be related to another in sequence. One example of such a device computes grapheme to phoneme relations. Transition labels such as


will be found in such a machine.Since there is no formal instantiation of structural constituency in finite-state machines, wider contexts have to be implemented by using (possibly large) sets of multi-symbol relations, e.g.


etc. lists an example of such a transducer.

Try it out:

?- [nfst1].

?- accept([s,q,u,e,a,k],_).

?- accept(X, [f,'0',k,s]).

The symbols on transition labels need not  be letters of the alphabet: any symbols will do. For instance, we could take vectors of acoustic analysis parameters (for instance a set of LPC predictor coefficients) as symbols. The alphabet of such symbols will be very large, but finite nonetheless. If we have a phonemically labelled speech database, we could relate phoneme symbols to analysis vectors, like this. Changes from one vector to another within a phoneme can be modelled as self-loops with phoneme:vector labels, like this. Changes from one vector to another at phoneme boundaries will be modelled as state-changing phoneme:vector transition labels. In a later seminar we shall see how large phoneme:vector finite state transducers can be constructed automatically from a labelled speech database. We could use such a machine for speech analysis (automatic segmental labelling or part of a speech recognition device) or for generation (synthesis). The main practical problems lie in the size of the machine needed in order to cover the space of analysis vectors fully.

Finite state transducers have also been employed extensively in modelling SPE-type rule systems, subject to certain common restrictions (see Kaplan and Kay 1994 and references), as well as in computational implementations of autosegmental phonology (see Bird and Ellison 1993 and references).

Finite-state syntactic processing

Centre embedding in a state transition network