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 (subsymbolic).
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.
Reading Lists
Books
** Should Be Purchased
S.J. Russell and P. Norvig (2003) AI: A Modern Approach
2. PrenticeHall 0130803022
** Recommended Text
I. Bratko (2001) Prolog for Artificial Intelligence
Third. AddisonWesley 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.. AddisonWesley 0805311963
P. Winston Artificial Intelligence
3rd Ed.. AddisonWesley 0201833774
S. C. Shapiro (1992) Encyclopedia of Artificial Intelligence
AddisonWesley
Notes
This module is at CQFW Level 5