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Next: MC214 Logic Programming Up: Year 2 Previous: MC208 Functional Programming

MC211 Automata, Languages and Computation


MC211 Automata, Languages and Computation

Credits: 20 Convenor: Prof. R. M. Thomas Semester: 1


Prerequisites: essential: MC111 or MC144 desirable: MC103 or equivalent
Assessment: Continual assessment: 30% Three hour exam in January: 70%

Lectures: 36 Classes: 12
Tutorials: 12 Private Study: 90
Labs: none Seminars: none
Project: none Other: none
Total: 150


Explanation of Pre-requisites

There is not very much in the way of pre-requisite knowledge required for this module. The main purpose of the course is to reason about the nature of computation; this is done by providing mathematical models for computational devices and then by investigating what tasks these abstract machines can perform. In order to form these models, we need the basic concepts of sets, relations and functions as introduced in MC111 and MC144; either of those modules provides all that we need for this course. In order to help understand the motivation for studying these models, it would be helpful to have done some programming before, such as that undertaken in MC103.

While previous experience of programming is desirable, it is not essential. Some of the methods in this module are expressed in a sort of pseudocode notation, but there is no actual programming content in the course, and a student who had not done programming before could still take this module if he/she wanted to. Such students are welcome to discuss their suitability for the course with the module convenor.

Course Description

In this course we are primarily concerned with what computers can do. It turns out that there are problems that cannot be solved by computer, or, at least, by machines corresponding to the mathematical models of computers we shall present. It is clearly sensible to investigate which problems cannot be solved - there is no point trying to program a computer to solve a problem that is unsolvable! We will give some precise mathematical models of the process of computation. Within these models, we will see what sort of tasks can be performed.

At first sight, it may appear that these models are unduly simple and do not really capture all the subtleties of the process of computation. The advantages of using such models is two-fold. First, they are very simple to reason about, so that we can reach our conclusions much more simply than (for example) considering actual hardware and software components in fine detail. Second, they have proved to be very robust, in that successive generations of computers have all been shown to be no more powerful than the most general model we will present, and so the analysis based on these models has been useful throughout the history of Computer Science, whereas an analysis based on the specifics of various machines and programming languages quickly becomes obsolete.

This material will be described in the lectures (three per week); there will also be a weekly tutorial session (for sorting out problems, including difficulties with the assessed work) and a problem class (for going through the previous worksheet). A full set of printed lecture notes for the module is also available.

Aims

The first aim of this course is to give an understanding of the basic theory of language recognition with applications in areas such as compiler design. The course will also aim to provide a general model of computation and thereby to illustrate the limits of the power of computers. By the end of the module the students should be able to comprehend abstract models of the process of computation and produce reasoned arguments about the power of such models. They should have somen awareness of the significance of these models in different areas of Computer Science.

Objectives

By the end of the module the students should be familiar with the fundamental models introduced in the course. They should be able to follow basic mathematical arguments couched in terms of these models and also be capable of constructing such arguments for themselves. They should be capable of writing such arguments clearly and correctly with proper use of mathematical notation. In addition, they should have understood the various specific techniques covered in the module (such as transforming regular grammars into finite automata and vice-versa, transforming a context-free grammar into an equivalent reduced context-free grammar, using the pumping lemmas to show that certain languages are not regular or not context-free, and so on) and be able to perform these techniques in practice.

Transferable Skills

The main purpose of this course is to provide mathematical models of the process of language recognition and computation. These are fundamental structures and grasping this material will allow students to pursue further study in areas ranging from compiler design through to computability.

Apart from the widespread use of these models, the study of the material in this course should help students develop their general ability to think abstractly and, consequently, provide them with the ability to be adaptable in a fast-changing subject area. In addition, the module should make a contribution to the general development of problem-solving skills.

Syllabus

Revision of mathematical pre-requisites (sets, relations and functions). Strings. Formal languages. Concatenation of strings. Kleene star.

Finite automata. Language acceptors. Regular languages. Equivalence. Complete automata. The concepts of determinism and non-determinism. The pumping lemma for regular languages. Examples of non-regular languages. Grammars. Terminals and non-terminals. Regular grammars. Equivalence of regular grammars and finite automata. Closure properties of regular languages. Empty moves. Regular expressions.

Stacks. Pushdown automata and context-free grammars. Syntax diagrams and EBNF. Parse trees. Leftmost and rightmost derivations. Equivalence of pushdown automata and context-free grammars. Reduced context-free grammars. Ambiguous grammars. Inherent ambiguity. Removing empty productions. Removing unit productions. Pumping lemma for context-free languages. Limitations of context-free grammars. Closure properties of context-free languages. Pascal is not context-free. Deterministic context-free languages. LL-parsers.

Turing machines. Extensions of Turing machines. Non-determinism. Decision-making Turing machines. Recursive languages. Existence of Turing acceptable languages that are not recursive. The halting problem. The Church-Turing Thesis. Further examples of unsolvable problems. Recursively enumerable sets. Equivalence of recursively enumerable and Turing acceptable. Phrase-structure grammars. Equivalence of phrase-structure grammars and Turing machines. The Chomsky hierarchy.

Reading list

Recommended:

J. G. Brookshear, Formal Languages, Automata and Complexity, Benjamin Cummings, 1989.

D. Kelly, Automata and Formal Languages - an Introduction, Prentice Hall, 1995.

Background:

D. Wood, Theory of Computation, Wiley, 1987.

D. Harel, Algorithmics, the Spirit of Computing, 2nd edition, Addison Wesley, 1992.

J. E. Hopcroft and J. E. Ullman, Introduction to Automata Theory, Languages and Computation, Addison-Wesley, 1979.

T. W. Parsons, Introduction to Compiler Construction, W. H. Freeman, 1992.

T. Sudkamp, Languages and Machines, Addison Wesley, 1988.

H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, Prentice Hall, 1981.

C. H. Papadimitriou, Computational Complexity, Addison Wesley, 1994.

D. I. A. Cohen, Introduction to Computer Theory, Wiley, 1986.

J. M. Howie, Automata and Languages, Oxford University Press, 1991.

Details of Assessment

The coursework for the continual assessment consists of weekly problem sheets throughout the module. Any problems with these can be discussed in the tutorials preceding the hand-in (or, if extra help is needed, by seeing the lecturer). Your work will be marked within a few days of submission and then gone through in the next problem class.

The written January examination will contain six questions. These are equally weighted; any number of questions may be attempted, but only the best four answers will be taken into account.


next up previous
Next: MC214 Logic Programming Up: Year 2 Previous: MC208 Functional Programming
S. J. Ambler
11/20/1999