After successful completion of the course, students are able to delineate the main application areas, formalisms, and methodologies for probabilistic modeling and reasoning in artificial intelligence.
The course gives an overview on the area of probabilistic modeling and 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.
