Code

COMP1009

Year

1or 2

Prerequisites

None

Term

1

Taught By

John Dowell (66.6%)
Denise Gorse (33.3%)

Aims

To introduce the study and development of intelligent systems, including the correspondence with natural cognitive systems and the design of smart tools.

Learning Outcomes

Students will understand foundational theories, methods, and technologies involved in artificial intelligence and in cognitive systems science. They will recognize the primary themes and issues involved in creating intelligent technologies and smart tools. The structural dimensions of intelligent agents will be examined in relation to the capabilities they support in different environments. Symbolic and sub-symbolic architectures will be examined and contrasted. Students will understand the theoretical and technical challenges involved in modeling and building systems that can reason, solve problems, acquire and use knowledge, make decisions, perceive their environment and communicate in natural language. They will learn how computational models can be used to help us understand human intelligence and about the smart technologies that extend that intelligence.

1. Introduction: cognition, systems and intelligence

2. Intelligent agents

3. Problem solving systems

4. Neural Networks and symbolic architectures

5. Knowledge representation systems

6. Decision making systems

7. Vision systems

8. Natural language systems

9. Computational models of human reasoning and action

10. Applications

Lecture presentations with associated seminars.

The course has the following assessment components:

• Coursework (4 elements) (100%)

To pass this course, students must:

Gain an overall mark of 40%

Russell and Norvig, (2010) Artificial Intelligence: A modern approach.

Module website