Logic	FORMAL SYSTEMS FOR PROBABILISTIC INFERENCE
Introductory course	MANFRED JAEGER and EMIL WEYDERT Max-Planck-Institut fuer Informatik, Saarbruecken
Both weeks	jaeger@mpi-sb-mpg.de and weydert@mpi-sb-mpg.de
Course description	The course explores various frameworks for probabilistic reasoning in formal systems, and their application in artificial intelligence. The study of systems that combine the representational rigor of formal logic with the processing of quantitative information goes back as far as Rudolf Carnap's work on inductive inference. In the context of artificial intelligence there has been a renewed and increasing interest in such formalisms. We will discuss various logical systems for reasoning about probabilities, ranging from simple systems for processing linear constraints on the probabilities of propositional formulas, to very expressive extensions of first-order and modal logics. We will discuss the nonmonotonic aspect of commonsense probabilistic reasoning, its similarities with nonmonotonic deductive reasoning, and its implementation by entropy maximization strategies. The course will also provide an introduction to Bayesian networks as a means of representation that often makes the inference problem tractable.
Prerequisites	None
Literature	Textbooks on the topic of this course are fairly scarce. "The Uncertain Reasoner's Companion" by J.B.Paris contains a part of the material we intend to present, and approximateley indicates the level of the course.