Module Identifier CS26210
Module Title THE ARTIFICIAL INTELLIGENCE TOOLBOX - PART II: PROGRAMMING IN AN UNCERTAIN WORLD
Co-ordinator Dr Simon M Garrett
Semester Semester 2
Other staff Dr Myra S Wilson, Mr David J Smith, Professor Qiang Shen, Dr Yonghuai Liu, Dr Simon M Garrett, Professor Mark H Lee
Pre-Requisite CS26110
Course delivery Lecture   20 Hours.
Practical
Assessment
Assessment TypeAssessment Length/DetailsProportion
Semester Exam2 Hours  100%
Supplementary Exam2 Hours Will take the same form, under the terms of the Department's policy.  100%
Further details http://www.aber.ac.uk/compsci/ModuleInfo/CS26210

#### Learning outcomes

On successful completion of this module students should 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

• explain the function and use of fuzzy logic

• explain the application of Bayesian probabilty to simple reasoning scenarios;

#### 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 follows on from the introduction to the fundamental concepts of AI given in CS26110 . 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

Knowledge representation [1 lecture]

All AI sets out to solve a problem. How do we represent problems and their solutions? We can use logic (symbolic) or numbers (sub-symbolic).

Logic and Reasoning (symbolic) [3 lectures]

What is logical representation and what can it do?
Inference: deduction, abduction and induction.

Prolog and Logic Programming [5 lectures]

Expert systems [2 lectures]

Uncertainty - Probability [4 lectures]

Probability (when things may or may not happen)
Probabilistic Reasoning and Baysian Nets.

Uncertainty - Fuzzy Sets and Systems [4 lectures]

Fuzzy sets (when things are partially true)
Fuzzy systems.

Books
** Should Be Purchased
S.J. Russell and P. Norvig (2003) AI: A Modern Approach 2. Prentice-Hall 0130803022
** Recommended Text
I. Bratko (2001) Prolog for Artificial Intelligence Third. Addison-Wesley 0201403757
M. Negnevitsky (2002) Artificial Intelligence Addison Wesley 0201711591
** Consult For Futher Information
C. J. Hogger (1990) Essentials of Logic Programming Oxford University Press 0198538324
G. F. Luger and B. Stubblefield (1997) Artificial Intelligence 3rd Ed.. Addison-Wesley 0805311963
P. Winston Artificial Intelligence 3rd Ed.. Addison-Wesley 0201833774
S. C. Shapiro (1992) Encyclopedia of Artificial Intelligence Addison-Wesley

#### Notes

This module is at CQFW Level 5