Module Identifier CS26210  
Module Title ARTIFICIAL INTELLIGENCE CONCEPTS  
Academic Year 2004/2005  
Co-ordinator Dr Myra S Wilson  
Semester Semester 1  
Other staff Dr Myra S Wilson, Dr Simon M Garrett, Dr Yonghuai Liu  
Pre-Requisite CS16010  
Course delivery Lecture   17 lectures  
  Practical   Up to 10 x 2hr  
Assessment
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours  100%
Supplementary Exam Will take the same form, under the terms of the Department's policy.   
Further details http://www.aber.ac.uk/compsci/ModuleInfo/CS26210  

Learning outcomes

On successful completion of this module students will be able to:


Brief description

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.

Aims

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.

Content

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.

Reading Lists

Books
** 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 Addison-Wesley

Notes

This module is at CQFW Level 5