Knowledge about the area of probabilistic reasoning in Artificial Intelligence.

The course gives an overview on the area of probabilistic reasoning in Artificial Intelligence. Some planned topics are summarized as follows: basics of probability theory; Bayesian networks; probabilistic logic; nonmonotonic probabilistic inference; probabilistic logic programming; decision theory; planning under uncertainty in Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs); game theory.

The course will be kept as a block from January 19 to 31. It will be given in English. Participants are kindly asked to register by email to the lecturer. Requirements of this course: elementary knowledge in logic. Further details on this course will be posted soon on this website.

Slides, exercises, selected solutions. Literature: Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 2nd Edition, 2003. Further literature is announced during the course.