- Dr Hong Wei (Associate Professor - University of Reading)
- Dr John Hunt (Associated Head of Department - University of the West of England)
|Delivery Type||Delivery length / details|
|Lecture||20 x 1 Hour Lectures|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||In class test (approx 15 hours)||20%|
|Semester Assessment||Written report (1,000 words), 15 hours||30%|
|Semester Assessment||Assignment (approx 30 hours) will cover knowledge representation, Prolog, and Expert Systems||50%|
On successful completion of this module students should be able to:
1. Describe the importance of propositional and predicate logic in Artificial Intelligence systems.
2. Solve simple problems in propositional and first order predicate logic.
3. Write Prolog programs to solve simple AI problems.
4. Explain the function and use of fuzzy logic.
5. Explain the application of Bayesian probabilty to simple reasoning scenarios.
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. Applications of AI techniques in computer security are discussed.
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, anomaly and intrusion detection, 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.
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]
Uncertainty - Probability (5 lectures plus 1 x 1 hour In-Class Test)
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)
This module is at CQFW Level 5