06-08775
Introduction to AI

Level 2

Dr J L Wyatt

10 credits in Sem1

Outline

The module provides an introduction to artificial intelligence for the benefit of students not taking a degree or half-degree in artificial intelligence. It provides knowledge that is useful in itself for computing researchers and practitioners, as well as providing essential background for more specialised artificial intelligence modules in the School. The main topics addressed (in outline) include: the relevance of AI both to contemporary computing applications and to other academic disciplines; the relative merits of various broad computation styles in AI (eg symbolic and neural); theoretical underpinnings of AI; major practical AI techniques; special programming requirements and approaches.

Aims

The aims of this module are to:

Provide a general introduction to artificial intelligence (AI)
Give an overview of knowledge representation and the main sub-fields of AI
Show the need for different knowledge representation formalisms
Develop an understanding of important AI techniques and their applications
Provide a foundation for further study of specific areas of AI

Learning Outcomes

On completion of this module, the student should be able to:	Assessed by:
Explain some of the main knowledge representation formalisms and algorithms used in AI	Examination
Apply them to example problems	Project and/or Examination
Discuss their advantages and drawbacks	Project and/or Examination
Explain some of the relationships between the subfields of AI and their techniques	Examination
Demonstrate a knowledge of search algorithms and their properties	Examination
Make informed decisions about what AI techniques to consider in particular domains	Project and/or Examination

Restrictions, Prerequisites and Corequisites

Restrictions:

Not available to students on the Artificial Intelligence & Computer Science degree or the half-degree in Artificial Intelligence.

Prerequisites:

None

Co-requisites:

None

Teaching

Teaching methods:

2 hrs lectures per week, 1 hr lab/exercise class

Contact hours:

Assessment

2 hr examination (70%), team project (30%). Resit by examination only with the continuous assessment mark carried forward.

Recommended Books

Title	Author(s)	Publisher, Date
Artificial intelligence: A New Synthesis	N Nilsson	Morgan Kaufmann, 1998
Artificial Intelligence: A Modern Approach	S Russell & P Norvig	Prentice Hall, 1995

Detailed Syllabus

Overview of the field: aims, methods, subfields, history, prospects.
Knowledge Representation formalisms.
Search: uninformed and informed techniques; properties.
Logic: Predicate Calculus, English -> FOPC.
Robotics: the Shakey project, behaviour based systems.
Neural Networks: Overview, Delta Rule, learning as search.
Computer Vision: Problems and low level operators.
Machine Learning: Classification using Decision Trees.
Evolutionary Approaches: the simple GA; GP; applications.

Relevant Links

See the Introduction to AI Web site.

Page maintained by:	Dr J L Wyatt
Content last updated:	29 January 2002
Source:	/resources/modules/2002/xml/08775.xml

06-08775 Introduction to AI