Due to a changes in lecturers, this text is still under revision.

The overall learning aims of the course are that students will be able to:

• Recognise typical AI problems, explain typical features of AI problems and think up examples of AI problems.
• Explain the relationship between a problem space and a search tree, explain fundamental search methods and their differences, produce an algorithm in pseudo code for elementary search techniques and select and apply search techniques in basic examples.
• Cite examples of different types of knowledge representation and produce effective types of representation for basic examples.
• Explain the methods for solving constraint satisfaction problems, and use them in basic examples.
• Explain the essence of different learning methods and elaborate on the details of these methods in basic examples.

The aim of the course is to give students a first impression of the field of Artificial Intelligence, without going into too much technical detail. The course will cover:

• What Artificial Intelligence is and an example of how AI techniques can be applied to an existing software system.
• Fundamental search techniques and their application.
• Types of knowledge representation, the main languages which are used and an example of their application in the field of Artificial Intelligence.
• Constraint satisfaction problems.
• Learning through computers, the main techniques which are used and an example of their application.