Introduction Artificial Intelligence A
Lectures, question-and-answer seminars and practice sessions.
Some knowledge of mathematics, programming and formal logic is beneficial.
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.
The following book is compulsory for AI students and is recommended for Computer Science students:
- Poole, D.I.R. & Mackworth, A.K.P. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, 2010, ISBN 978-0-521-51900-7.
(The entire book is legitimately available via http://artint.info/html/ArtInt.html).
|SWS / PersoonlijkRooster|