# Inf1A: Deterministic Finite State Machines

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1 Lecture 2 InfA: Deterministic Finite State Machines 2. Introduction In this lecture we will focus a little more on deterministic Finite State Machines. Also, we will be mostly concerned with Finite State Machines which are acceptors (ie, have no output alphabet) for this lecture. The idea is that we will explore deterministic FSMs in more depth, giving formal definitions of things that were informally discussed in Lecture. We will also discuss the binary representation of natural numbers in this lecture. All modern digital computers store data such as natural numbers, floating point numbers, and letters of the alphabet in binary representation: that is, in terms of sequences of 0s and s. 2.2 Formal Definitions Before we launch into our formal definition of deterministic FSMs, it is useful to know a bit about some of the words we will use throughout the definition. An alphabet is just some set containing a finite number of distinct symbols. When we talk about an alphabet, we are usually interested in strings over that alphabet. A string over is simply a sequence of any number of symbols taken from. We write to represent the set of all strings over. For example, if the alphabet is c d, then all the following are elements of : c d cc cd dc dd ccc ccd cdc In general contains strings of any length which are made up of cs and ds. Notice that one of the strings is special. This is the string, which is the empty string, which contains no symbols from. The empty string is an important member of for any alphabet. For any alphabet, a language over is a subset of (a set such that every string in belongs also to ). For example, the set c containing all strings made up of cs is a language over c d. The set c ddc is also a language over c d. 7

2 Computation & Logic: Lecture 2 on Computation Informatics A (2004) Here is the formal definition of an acceptor-type deterministic Finite State Machine. Definition (Deterministic FSM) A deterministic Finite State Machine (D-FSM) of the acceptor type is a machine as a 5-tuple consisting of defined. a finite set of states 2. a finite alphabet 3. a distinguished start state 4. a set of accepting states, and 5. a description of all the possible transitions of the FSM. The usual formal definition given for deterministic FSMs states that the set of transitions of the machine should be a function. We refer to this function as the transition function of the machine. Notice that item 5. of the definition given above implies that at any state of a D-FSM, (because we must have exactly one transition leaving for every different symbol when we talk about a function with the domain, this means that must be defined for all possible values of and ). In practice we can be a little bit more flexible than that. Remember from Lecture that our goal in defining D-FSMs was to ensure that for every input string, there is at most one trace in for. We achieved this with a strict definition of the transitions allowed. If, then the fact that there are no -transitions in a D-FSM implies that (it has at least one symbol), there is exactly one trace for (which is the trace ). If there is exactly one list of states! " (where is the start state) such that #%\$ #&" " is a trace of. This is because since is a function, therefore for every state and every symbol, there is exactly one transition leaving labelled with (the state given by ), and no -transition leaving. Therefore at any state of the machine reading any symbol, there is no choice (no non-determinism) for the next state - it is given by. The trace above is given by \$ ' # \$ " "'( \$ #)"'( \$. * However, our transitions do not have to define a function on in order to avoid, there is at most one multiple traces - it is enough that for every and every such that there is an transition from to while reading the symbol. Therefore + we will sometimes allow the transition function to be undefined for some elements of. Definition (Language accepted by a D-FSM), Let accepted by be a deterministic FSM of the acceptor. Then the language is the set of strings such that (i) there is a trace for in ; (ii) the trace ends in an accepting state (a state from ). 8 -

3 Computation & Logic: Lecture 2 on Computation Informatics A (2004) Observe that for any D-FSM whose set of transitions is a function, it is always the case that (i) above holds (there is a trace for every string ). Therefore for any, is rejected if and only if the last state in its trace is a non-accepting state (the last state is not in ). It would be nice if every D-FSM was a equivalent to a D-FSM which satisfied the strict conditions of the Definition given above. We now show that this is indeed the case. Let be a deterministic FSM whose set of transitions is not strictly a function. Therefore there may be some strings which are rejected (not accepted) by the machine because there is no trace for the string - this means that at some point during the computation of on, there is no transition leaving the current state which is labelled with the next symbol of the input string. Now suppose we alter by adding a new state to - we will call this a sink state. The sink state will not be an accepting state. Now for every state and every symbol such that there is no transition leaving labelled with, we add such a transition, using the sink state as the target state. Therefore, we will direct all strings which have no trace in to this new (non-accepting) sink state. We also add a transition from the sink state to itself labelled with all of the symbols of the alphabet. This ensures that the transition function is complete on the extended state set, and that once a string is sent to the sink state, it will stay there. Therefore every string that had no trace in the machine now has a trace in the extended FSM, and most importantly, every such string is rejected by the new machine. So the language of the new machine is identical to. 2.3 Binary numbers and Unary numbers Throughout your years in high school you are probably used to writing the natural numbers as a sequence of digits from. These digits are known as the decimal digits. In the terminology of Section 2.2 we could say that the set of decimal numbers is the set of strings over the alphabet (as long as we identify the empty string with the number ). This is the language. In fact (better), we could say that the set of decimal numbers is the set of strings over the alphabet whose leading digit is not. In this section we will present two alternative systems for representing natural numbers: (i) the binary numbers (which is the representation that digital computers use for natural numbers), and (ii) a more unusual (and much less compact) number system, which is the unary number system Decimal numbers For any natural number, we can represent in the decimal system by some sequence of decimal digits " ( \$ \$, where the digits \$ " ( \$ are chosen so that " ( \$ \$ " ( \$ " ( \$ The natural numbers are just the counting numbers, that is:! "\$#%\$&%'' ' and so on. 9

4 Computation & Logic: Lecture 2 on Computation Informatics A (2004) The value is just a variable to represent the length of the string. This representation is the way you are used to seeing numbers written. Of course the representation is unique (there is exactly one decimal representation for each natural number, unless you decide to add some useless s after the final digit " ( \$ ). We represent by the empty string (or by ). As we know well, the leading digits are the digits which contribute most to the value of - we say that the digit " ( \$ (assuming it is non-zero) is the most-significant digit and that is the least-significant digit Binary numbers Suppose that we only are allowed to work with the binary alphabet, and that we need a way of representing natural numbers using strings over this alphabet (that is, we have, and we have to work with strings from ). We show that we can represent all natural numbers by binary strings. For every binary string " ( \$ \$, where \$ " ( \$ (and where is just a variable to allow us to talk about the length of the string), the number represented by " ( \$ \$ is the natural number " ( \$ \$ " ( \$ "'( \$ For every natural number, the binary representation of that number is unique, if we insist that there are no leading s. Again '" ( \$ is the most significant digit (assuming it is non-zero) and is the least significant digit. It is not difficult to come up with the binary representation of a natural number - you can find the binary number by successively dividing by and taking the remainders (each is either or ) in reverse order as the basis of the binary representation. For example, if we consider, then performing this procedure gives: 53 original number 26 dividing by gives div, rem 3 0 dividing by gives div, rem 6 dividing by gives div, rem 3 0 dividing by gives div, rem dividing by gives div, rem 0 dividing by gives div, rem Then, reading the remainders off in reverse, the binary representation of is 00. It is an interesting exercise to try to give a formal argument that this procedure always constructs the correct binary representation of. Binary numbers play a central role in Computer Science, and it is fun to see how to add, subtract, multiply and divide binary numbers (without converting into a decimal representation). There is some information about this at the Wikipedia link at the end of these notes. 0

5 Computation & Logic: Lecture 2 on Computation Informatics A (2004) Unary numbers Finally, we mention a very inefficient representation of natural numbers. Suppose our alphabet only contains one symbol, that is, Suppose we want to represent the natural numbers as strings over. In this case, we have the following representation: if if where in the case, we repeat exactly times. 2.4 Deterministic FSMs to recognise odd numbers We will now consider the task of designing a deterministic FSM to recognise odd natural numbers. We will construct three different FSMs - one for numbers in unary notation, one for numbers in binary notation and finally, an FSM for numbers in decimal notation. Odd numbers in unary To construct the machine for odd unary numbers, remember that our input alphabet is. Consider what happens for the empty string - this represents, which is not odd. So the start state of our machine, labelled below, will not be an accepting state. Notice that whenever we read an extra from the input string, we switch back and forth between having seen an odd number of s and an even number of s. Therefore we really only need to have two states for our machine. The second state, labelled, will represent the state of having seen an odd number of s so far. 2 Figure 2.: Machine to recognise odd numbers in unary It is a simple task, but worthwhile, to test the machine above on some short strings over. Notice that the FSM in Figure 2. is deterministic, and has a complete transition function. Odd numbers in binary In this case our input alphabet is 0 In order to recognise odd numbers in binary, we need to make a decision about the order in which the binary number is input to the FSM. We will require that the binary string is presented in the order of least-significant bit first. Now think about the binary representation described in Section 2.3. The natural number represented by the binary string '" ( \$ is \$ " ( \$ " ( \$. Notice that every term in this summation is multiplied by (and greater powers of ) except for

6 Computation & Logic: Lecture 2 on Computation Informatics A (2004). Therefore the natural number is odd if and only if the least the least significant bit significant bit is equal to - if we see that the least significant bit is, then we do not need to check anything else. Here is our first attempt at doing the FSM. Notice that the start state is not an accepting state. We have a second state, labelled, which is accepting. We draw the transitions in such a way so that we only transition to this accepting state if the first bit read from the input string (the least significant bit) is. 0, 2 Figure 2.2: Machine to recognise odd numbers in binary It is a good exercise to test the strings 000 (the number ) and 000 (the number ) on the machine in Figure 2.2. The testing phase is always useful for debugging FSMs. Notice that the machine in Figure 2.2 is deterministic. There are no -transitions, and all transitions leaving the same state are labelled with different symbols (or bits, in this case). Suppose we draw a table to represent the transitions of the FSM in Figure 2.2: 0 state - state state state state Notice from this table that the set of transitions for our D-FSM of Figure 2.2 is not complete. It is not a function on. However, using the sink state construction from the end of Section 2.2, we can construct an equivalent D-FSM with a transition function to recognise odd numbers in binary. This new D-FSM is shown in Figure 2.3. The sink state is the state. 0, , Figure 2.3: Machine 2 to recognise odd numbers in binary The machine in Figure 2.3 accepts exactly the same language as the machine of Figure 2.2, which is the language of binary strings with a in the least-significant position. 2

7 Computation & Logic: Lecture 2 on Computation Informatics A (2004) 2.4. Odd numbers in decimal Here is a machine to recognise odd numbers in decimal. It is a good idea to test it out, and to draw the transition-table. 0,,2,...,9,3,5,7,9 2 0,2,4,6,8 3 0,,2,...,9 Figure 2.4: Machine to recognise odd numbers in decimal 2.5 Finite State Machines in Haskell We will now see how we can implement some of our little Finite State Machines from Section 2.4 in Haskell. The first thing to say is that in general the problems that we have been modelling with Finite State Machines are much simpler than the computational tasks which you have been solving in the FP part of this course. However, one of the ways of implementing Finite State Machines involves using mutual recursion, which is an interesting concept in Functional programming. Consider our first FSM for recognising odd numbers in unary notation. This machine has two states - the start state, named, indicates that the part of the string read so far consists of an even number of s, and the other state, named, indicates that the part of the string read so far consists of an odd number of s. We define two mutually recursive functions oddun and evenun to represent our two states. Typing in oddun with a string of s returns True if the string consists of an odd number of s and False if the string consists of an even number of s. It is a nice exercise to define an appropriate data type to ensure that the only lists passed to oddun are lists of s. oddun :: [a] -> Bool evenun :: [a] -> Bool oddun [] = False oddun (x:xs) = evenun xs evenun [] = True evenun (x:xs) = oddun xs 3

8 Computation & Logic: Lecture 2 on Computation Informatics A (2004) 2.6 Summary In this lecture note we have Given a formal definition of a deterministic FSM and of the language accepted by a deterministic FSM. Given an introduction to binary representation for natural numbers as well as unary notation. Given deterministic Finite State Machines to recognise odd natural numbers represented in (i) unary notation; (ii) binary notation; (iii) and decimal notation. Discussed the issue of whether the set of transitions of a D-FSM form a complete transition function on. Shown that we can ensure that a deterministic machine has its transitions described by a transition function by simply adding one sink state to the machine. Discussed the issue of testing FSMs for correctness. 2.7 Extra work We didn t cover binary numbers in much depth (just enough to suit our purposes for the examples of Finite State Machines given above). Wikipedia has a very useful page on the Binary number system. It is located at: numeral system Draw the transition table for the D-FSM for accepting odd numbers in decimal notation. Also write a set of three mutually recursive Haskell functions to implement the machine of Figure 2.3, and another set of Haskell functions to represent the machine of Figure 2.4. These notes have been adapted from two sets of earlier notes, the first due to Stuart Anderson, Murray Cole and Paul Jackson, and the second due to Martin Grohe and Don Sannella. Mary Cryan, November

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