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1.1. About this Course and this Book

1.1.1. Introduction

This eTextbook is intended for a senior-level course in Formal Languages and Automata (FLA). It follows a well-established collection of topics, as traditionally taught in most FLA courses. However, it does it in some untraditional ways, as explained below. Also, since this might be the only classical CS theory course that you take in your curriculum, we throw in at the end a little introduction to two other traditional topics in CS theory: Complexity theory (also known as NP-completeness, which relates to undestanding what is expensive versus not so expensive to compute); and Computability (the study of where the limits are in terms of what problems are possible for a computer program to solve).

1.1.2. Prerequisites

This is a CS “Theory” course. In practice, that means using mathematics to help undestand deep CS concepts, and how they relate to practical CS applications. This course assumes that you already have sufficient background in a number of Math and CS topics.

You should have had a course in Discrete Math, covering at least the following:

You should have had a course in Data Structures, covering at least the following:

  • Basic algorithm analysis, including big-oh, big-Omega, and Theta notations.

  • Basic data structures and algorithms including lists, search structures such as BSTs, sorting algorithms, heaps, hashing, and standard graph algorithms.

1.1.3. How this Book Works

This book is not like your typical textbook, in a lot of ways. You might have previously used an OpenDSA eTextbook for another class. If so, some of this will be familiar. But even if you have used OpenDSA before, some basic features in this book will probably be new to you.

First, a lot of the content is presented using a technique called “Programmed Instruction”. The idea beind Programmed Instruction is to keep you engaged with the material by constantly asking you questions about what you are reading. So what you will see is a type of slideshow, that we call a “Programmed Instruction frameset”. Each slide in the frameset will present a small amount of information, perhaps with some graphics to help make things clear. But frequently, to make progress in the frameset, you will have to answer a question. We hope that you will find these to mostly be easy questions (at least, most of them are supposed to be easy). Their goal is to keep you focused on what the content means, forcing you to learn it as you go by constantly challenging your understanding. So, besides keeping you engaged, being able to answer the questions as you go through the content means that you can be confident that you really understood what you read.

Second, this book is filled with many auto-graded exercises. They also help to make sure that you understand what you have read, at a more integrated level than the simple questions given in the framesets.

Third, since this book all about simple types of “machines”, we have provided many tools to create and (visually) simulate the behavior of these machines. And we integrate these simulations into some of the exercises. This is a lot like writing small programs. It’s just that instead of writing in a normal programming language like Java, you will be writing with a machine editor. You machine is typically expressed as a graph. Sometimes you will define a different type of “machine” by writing a grammar. But it’s a lot like programming either way, and internally we verify that your machine is correct by running unit tests against your machine to see if its answers match ours.

1.1.4. What We Will Do

It is really hard to reason about the abilities of a modern Intel or AMD processor with billions of transistors. And you don’t want to reinvent the wheel if you can process your input with a regex parser or use a tool like YACC to generate a simple compiler. So, to help understand these problems, and the tools that can solve them, Computer Scientists have developed many simple models of computation. Each of them can be implemented relatively easily in software. But more importantly, they are simple enough to really understand what they can (and cannot) do.

This course is about these various models of computation, how complicated each one is, and what its limits are. For example, if you know what you can or cannot do with a regular expression, then maybe you can solve a hard problem with simple calls to a regex library. On the other hand, maybe you can avoid wasting your time trying to use regex tools to solve the wrong problem (not all sets of strings can be represented by a regex). Likewise, if you know the limits of the types of grammar supported by a given compiler generator like YACC or Bison, then you know whether you can use that tool to get your job done, or whether you need to go to the much greater effort of “rolling your own” compiler. This sort of question comes up often enough in the working life of a practicing programmer that you want to know when a tool will solve your problem and when it will not.

By the end of this class, you will be able to answer questions like the following.

  • Can you write a program to determine if a string is an integer?

    • Examples: 9998.89 8abab 789342

    This should be easy. Think about how you would solve it with a program.

  • Can you do it if your machine had no additional memory other than the program itself? That is, you can’t store any values (no variables!) or look back at the input again.

    Answer: Yes. You can solve this by looking at the symbols one at a time, left to right, without looking back at a previous symbol and without using any variables to keep track of anything.

  • Can you write a program to tell if a string has an odd number of characters?

    Of course, this is easy.

  • Can you do it without any working memory?

    Answer: Yes. Here we get into the concept of an “even state” and an “odd state”. But those can be built into the program, so need not be part of the working memory.

  • Can you write a program to determine if a string is a legal arithmetic expression?

    • Examples:

      • ((34 + 7 ∗ (18/6)))

      • (((((((a + b) + c) ∗ d(e + f)))))

    How would you solve this? What do you need to keep track of?

    One sub-problem is balanced parentheses. Can you just determine if there are the correct number of parenthesis, and that they are in a legal order? (()(())) is good, but ())( is not good. For this, a stack will let you do the job.

    But, can you solve this simpler than that, without a stack? You can actually do this with one integer variable: add one for left paren, subtract one for right paren. The requirements are that the count starts at 0, never goes negative, and ends with 0.

  • But, can you do it if your machine had no additional memory other than the program itself? That is, you can’t store any values or look at them again.

    The answer is NO, you have to have memory (at least an integer variable) to keep track of left versus right parenthesis. There is no way to solve this problem without extra memory. We can’t use the “state” trick mentioned above, because there are an unlimited number of possible “states” (one “state” for each value of the integer variable).

  • Could you solve this problem (without memory) if you were limited to look at expressions of length 12 or less?

    YES you could. The alphabet must be finite, say \(N\) characters. How many possible strings are there to check? \(N^{12}\), some which are valid, some which are not. Your program would be brute force and incredibly huge and be of the form “If x is this string, then valid, else if x is this string, then not valid, etc.” But it is possible to do.

    Another approach is to use state to keep track of how unbalanced you currently are. In this case, this works because we can’t have more than 12 such states. So, this is another solution that does not need working memory.

    By the way, this idea of trying to solve problems without working memory probably seems alien to you in the context of writing programs. But we are going to see other ways of doing computation (in particular, checking if a given string is in some set of strings or not), and these methods do not use working memory in a way that will seem quite natural within their context.

  • Can you write a program to determine the value of a valid mathematical expression?

    • Example:

      • ((34 + 7 ∗ (18/6)))

    This question is different. Instead of asking if the form of an expression is valid, we are asking to evaluate it and solve it (well, of course that will only successfully happen if the expression happens to be valid).

  • But, what memory or computational power is required? Does the ability to recognize if a string is a valid mathematical expression have the same level of power required to compute the result of that expression?

    Answer: No.

  • Can you write a program to determine if a file is a valid Java program?

    This is what a Java compiler does! It first determines if the program is valid Java. If so, it turns the program into a form that is more efficient for the computer to execute. Finally, (at least, when you ask it to) it executes the program.

  • Can you write a program to determine if a Java program given as input will ever halt?

    The input is a Java program and the output is whether or not the program will halt. How might such a program work?

    What constructs in a program make it difficult to determine if the program will halt or not? Loops can be difficult to determine as they might not be obvious that they terminate. Recursion can also be hard to tell if it stops. And we can have direct recursion (a function calls itself), or indirect recursion (a function calls another function which then calls the first function). Just focusing on loops, how do you determine if a loop condition will be met so that the loop halts? This is a very hard problem to solve. (That is wrong. Actually, this is an impossible problem to solve in the general case. It is not possible always to tell if a loop will halt.) This is another topic that we will be looking at this semester: What functions are possible to compute?

  • What types of languages can we represent with Regular Expressions, BNF Grammars, and Context Free Grammars? Are these all “the same” in that they deal with the same languages, or are they different?

  • What is the relative “power” of a Push-down Automata, a Finate State Automata, a Non-Deterministic Finite Automata, and a Turing Machine? For any pair of these, are there problems that one can solve that the other cannot?

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