Module 18184 (2004)

Syllabus page 2004/2005

06-18184
AI & Cognitive Science

Level 1/C

William Edmondson:5
Unknown/Left:15
John Barnden:10
William Edmondson (coordinator)
10+20 credits in Semester 1 and Semester 2

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.)

Changes and updates

New module for 2004/05 (in part replaces AI Techniques B and Logic).


Relevant Links

See the Module Web Page for module information and resources.


Outline

This module provides a general introduction to Artificial Intelligence and Cognitive Science, including an introduction to each of their main subfields. The focus of the module is on AI as a science of intelligence.


Aims

The aims of this module are to:

  • provide a general introduction to the techniques and theories of Artificial Intelligence and Cognitive Science, building on material provided elsewhere
  • present Artificial Intelligence as a computational theory of intelligence
  • give a deeper understanding of themes such as representation, heuristics and search that underly the main subfields of AI
  • introduce logic as a formalism for representation and reasoning
  • demonstrate the need for different approaches for different problems

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1describe the ideas, issues, problems and techniques in some of the main subfields of Artificial Intelligence and Cognitive Science, including Cognitive Psychology, Search, Rule Based Systems, Logic, Reasoning, Vision, Robotics, Natural Language Processing and Adaptive Computation Continuous assessment, examination
2identify and describe some basic structures and mechanisms forming the biological basis of intelligent behaviour Continuous assessment, examination
3explain and discuss some computational models in Cognitive Science Continuous assessment, examination
4discuss the philosophical issues arising from such computational models Continuous assessment, examination
5explain the most important knowledge representation formalisms and why they are needed, discussing their advantages and disadvantages Continuous assessment, examination
6apply these knowledge representation formalisms to example problems Continuous assessment, examination
7employ the first order predicate calculus as a formalism for representation and reasoning Continuous assessment, examination
8describe the uses and limitations of logic in AI and discuss alternatives Continuous assessment, examination
9describe, analyse and critically discuss a variety of AI techniques and apply them to example problems Continuous assessment, examination
10provide examples of AI systems and applications, and explain common techniques, differences and limitations Continuous assessment, examination
11explain and apply simple experimental techniques to AI and Cognitive Science problems Continuous assessment, examination

Restrictions, Prerequisites and Corequisites

Restrictions:

None

Prerequisites:

None

Co-requisites:

06-18188 (Introduction to AI), 06-18185 (AI Programming)


Teaching

Teaching Methods:

3 hrs/week of lectures and exercise classes in Semester 1, 4 hrs/week in Semester 2

Contact Hours:

Approximately 81


Assessment

  • Supplementary (where allowed): As the sessional assessment
  • 2 hr examination (60%), continuous assessment (40%). Resit by examination only.

Recommended Books

TitleAuthor(s)Publisher, Date
Artificial Intelligence: A Modern Approach (2nd edn)S Russell & P NorvigPrentice Hall, 2003
Artificial Intelligence: A New SynthesisN J NilssonMorgan Kaufmann, 1998
Artificial IntelligenceRob CallanPalgrave Macmillan, 2003

Detailed Syllabus

  1. Cognitive Psychology
  2. Cognitive Science
  3. Informed Search and Planning
  4. Rule Based Systems
  5. Propositional Logic
  6. Predicate Logic
  7. Resolution theorem proving
  8. Vision and Robotics
  9. Natural Language Processing
  10. Experimental techniques
  11. Reasoning
  12. Knowledge Representation
  13. Limitations and Misconceptions of AI
  14. Philosophical Issues

Last updated: 5 Nov 2004

Source file: /internal/modules/COMSCI/2004/xml/18184.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus