344.014, VO, 2std., WS 2008/09
Univ.-Prof. Dr. Gerhard Widmer
This class will be taught in English.
TIME: Monday, 12:00 - 13:30
START: Monday, 6.10.2008
PLACE: HS 7
ANMELDUNG: im KUSSS
Goals and Contents of this Class:
The lecture gives an introduction to basic models and techniques in the
field of Artificial Intelligence (AI). Specific topics to be covered include:
- Definitions of AI, history of AI, current state of the field
- Motivating scenario: autonomous intelligent "agents"
- Problem solving as a search process:
- uninformed search algorithms
- heuristic search algorithms
- Knowledge representation and logical inference:
- Propositional logic
- First-order (predicate) logic
- Representing and reasoning with uncertain knowledge:
- Basics of Bayesian probability theory
- Knowledge representation and inference in Bayesian networks
- Basic notions of machine learning:
- Learning logical definitions: inductive concept learning
- Learning strategies for intelligent action: reinforcement learning
Pdf versions of the Powerpoint slides used in the lecture will be made
available via KUSSS (weekly). If desired, printed copies can also be bought
at the Department of Computational Perception (upon prior notice).
Recommended reading (will not be needed if the lectures are
attended on a regular basis):
Russell, S.J. and Norvig, P. (2000).
Artificial Intelligence: A Modern Approach (2nd. Edition).
Englewood Cliffs, NJ: Prentice-Hall.
Exercise track (‹bung):
The class is accompanied by an
exercise track (344.021, 344.022, 344.023),
in which the students will improve their understanding of the material
by regularly solving example problems (on paper).
Be sure to also register for one of 344.021, 344.022, 344.023!
Questions, suggestions, complaints, etc. to:
gerhard dot widmer at jku dot at
edited by gw on