On successful completion of this module students will be able to:
describe the importance of propositional and predicate logic in Artificial Intelligence systems;
solve simple problems in propositional and first order predicate logic;
write Prolog programs to solve simple AI problems;
select and defend the use of particular search techniques in the solution of problems such as path planning;
explain the function and use of fuzzy logic;
explain and understand the use of evolutionary computation;
explain the application of Bayesian probabilty to simple reasoning scenarios;
explain and apply variuos machine learning techniques.
Artificial Intelligence (AI) has made many important contributions to computer science in general, and most experts believe AI techniques will become increasingly important. This module builds on the introduction to the fundamental concepts of AI given in CS16010 . Key issues including knowledge representation, reasoning and the problem of approximate information are addressed both theoretically and practically.
Artificial Intelligence is the study of computer systems which can perform the sort of tasks that are usually associated with human intelligence. Examples are: chess playing, pattern recognition, speech understanding and problem solving. The aim of this module is to introduce the main ideas and current problems in Artificial Intelligence including the key concepts of knowledge representation, reasoning and the problem of approximate information. Students will be required to implement and utilise these concepts by means of an Artificial Intelligence programming language.
1. Introduction - 1 Lecture
Revision of AI material covered in CS16010.
2. Introduction to Logic and Prolog - 2 Lectures
3. Knowledge Representation and Inference - 3 Lectures
Propositional Logic and First Order Predicate Logic.
4. Advanced Search - 4 Lectures
Examination of the more advanced aspects of search. Evolutionary search.
5. Uncertainty - 4 Lectures
Probabilistic Reasoning, Fuzzy Logic.
6. Learning - 2 lectures
Logical learning from observations. Decision trees revisited in the light of inductive learning. Using information theory.
7. Summary - 1 Lecture
Review and analysis of AI.
A. Prolog - up to 10 Practicals
Workshops on programming in and using Prolog.
** Recommended Text
I. Bratko (2001) Prolog for Artificial Intelligence
Third. Addison-Wesley 0201-40375-7
M. Negnevitsky (2002) Artificial Intelligence
Addison Wesley ISBN 0201711591
** Should Be Purchased
S.J. Russell and P. Norvig (2003) AI: A Modern Approach
2. Prentice-Hall 0-13-080302-2
** Consult For Futher Information
C. J. Hogger (1990) Essentials of Logic Programming
Oxford University Press ISBN 0-19-853832-4
P. Winston Artificial Intelligence
3rd Ed.. Addison-Wesley 0-201-83377-4
G. F. Luger and B. Stubblefield (1997) Artificial Intelligence
3rd Ed.. Addison-Wesley ISBN 0805311963
S. C. Shapiro (1992) Encyclopedia of Artificial Intelligence
This module is at CQFW Level 5