CS 6766 - [Reasoning about Uncertainty]

This course discusses modeling and reasoning about uncertainty, going from purely qualitative notions (an event is either possible or it is not) to quantitative notions such as probability (an event has probability .8), with some consideration of in-between notions of plausibility. We consider various logics of reasoning about uncertainty, both propositional and first-order, and discuss the subtleties they reveal. Finally, we discuss how our approaches give us tools to understand and analyze central problems in the literature, including nonmonotonic reasoning, belief change, counterfactual reasoning, and problems of statistical inference, particularly that of going from statistical information to degrees of belief. Although many of the examples will be drawn from the AI literature, the material is also relevant to distributed systems, philosophy, statistics, and game theory; I will try to make connections to work in all these areas.

The course follows closely the material in the book Reasoning About Uncertainty, which actually was inspired by early versions of the course.