Module 18188 (2006)
Syllabus page 2006/2007
06-18188
Introduction to AI
Level 1/C
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
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Module Web Page
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Outline
This module provides a general introduction to artificial intelligence, its techniques, and main subfields. The principal focus of the module will be on the common underlying ideas, such as knowledge representation, rule based systems, search, and learning. It will provide a foundation for further study of specific areas of artificial intelligence.
Aims
The aims of this module are to:
- provide a general introduction to artificial intelligence, its techniques and its main subfields
- give an overview of key underlying ideas, such as knowledge representation, rule based systems, search, and learning
- demonstrate the need for different approaches for different problems
- provide a foundation for further study of specific areas of artificial intelligence
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | recognise the important features of AI systems, and structure the field of AI into its main subfields | Examination |
| 2 | explain some of the most important knowledge representation formalisms and why they are needed, discussing their advantages and disadvantages | Examination |
| 3 | apply these knowledge representation formalisms to simple unseen examples | Examination |
| 4 | describe and apply some simple search algorithms | Examination |
| 5 | outline the processes involved in rule-based Expert Systems and in building such systems | Examination |
| 6 | discuss the importance of learning in intelligent systems, and how it might be implemented | Examination |
| 7 | provide examples of different types of AI systems, and explain their differences, common techniques, and limitations | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
3 hrs/week of lectures, guest seminars, and exercise sessions
Contact Hours:
Assessment
- Sessional: 1.5 hr examination (100%).
- Supplementary (where allowed): As the sessional assessment
Recommended Books
| Title | Author(s) | Publisher, Date |
| Artificial Intelligence | Rob Callan | Palgrave Macmillan, 2003 |
| Artificial Intelligence (2nd edn) | E Rich & K Knight | McGraw Hill, 1991 |
| Artificial Intelligence: A Modern Approach (2nd edn) | S Russell & P Norvig | Prentice Hall, 2003 |
| Artificial Intelligence: A New Synthesis | N J Nilsson | Morgan Kaufmann, 1998 |
| Expert Systems (3rd edn) | P Jackson | Addison Wesley, 1999 |
| Artificial Intelligence (3rd edn) | P H Winston | Addison Wesley, 1992 |
| Artificial Intelligence | M Negnevitsky | Addison Wesley, 2002 |
| Artificial Intelligence (5th edn) | G Luger | Addison Wesley, 2005 |
Detailed Syllabus
Not applicable
Last updated: 2 Oct 2006
Source file: /internal/modules/COMSCI/2006/xml/18188.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus