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.
