Reasoning About Uncertainty
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Reasoning About Knowledge
Reasoning About Uncertainty (CS 6766):
Agents must reason and act in an uncertain world.
In order to do so intelligently, they need to deal with and
reason about this 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.