COMP209P - Cognitive Systems and Intelligent Technologies
This database contains 2016-17 versions of the syllabuses. For current versions please see here.
|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 modelling 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
Method of Instruction:
Lecture presentations with associated seminars.
The course has the following assessment components:
- 2-hour 'seen' examination - i.e. written examination by prior disclosure (100%)
To pass this course, students must:
- Obtain an overall pass mark of 40% for all components combined.
Russell and Norvig, (2010) Artificial Intelligence: A modern approach.