Undergraduate Programme and Module Handbook 2007-2008 (archived)
Module COMP3311: ADVANCED ARTIFICIAL INTELLIGENCE (20 CREDITS)
Department: Computer Science
COMP3311: ADVANCED ARTIFICIAL INTELLIGENCE (20 CREDITS)
| Type | Open | Level | 3 | Credits | 20 | Availability | Available in 2007/08 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- Software Applications (COMP2071).
Corequisites
- None.
Excluded Combination of Modules
- Advanced Artificial Intelligence (40 credits) (COMP3352).
Aims
- To give students a familiarisation with an understanding of a range of subjects within the broad context of Artificial Intelligence.
Content
- Multi-agent systems: design of intelligent agents.
- framework for programming agents.
- multi-agent interactions.
- cooperation and other interactions.
- Advanced information systems: data modelling.
- web data management.
- Semantic and Knowledge management.
- web semantic/ontology languages.
- Advanced topics in AI: Natural language processing.
- learning.
- reasoning under uncertainty.
- planning.
- the associated problem-solving paradigms and knowledge representation schemes.
- Mechanised Reasoning: Introduction to mechanised reasoning.
- the logical bases of reasoning methods.
- automated and interactive reasoning.
- systems and applications of mechanised reasoning.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the modules students will: Understand how Artificial Intelligence methods impact on the different problem areas in two topic areas.
- Have knowledge of different paradigms and techniques relevant to each of the two topics.
- Understand how Artificial Intelligence is relevant to new technology and technological developments in each of the two topics.
Subject-specific Skills:
- Demonstrate, for each of the two topics, that they have conducted research and self-study to further their knowledge beyond the taught materials.
Key Skills:
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lecturing demonstrates what is required to be learned and the applications of the theory to practical examples.
- Homework problems identify areas where further independent research should be conducted.
- Summative examinations test the knowledge acquired and the students' ability to use this knowledge to solve complex problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 19 | 2 per week | 2 hours | 38 | |
| Reading and Preparation | 162 | ||||
| Total | 200 |
Summative Assessment
| Component: Coursework | Component Weighting: 30% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| coursework | 100% | ||
| Component: Examination | Component Weighting: 70% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| One and-a-half hour examination | 100% | ||
Formative Assessment:
Example exercises given through the course.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University