Attempts to use automated learning and decision making for real world problems have led to increasing appreciation for the need for planning under uncertainty. This introduction is organized around four common types of uncertainty---effect, outcome, state, and opponent---and describes how they interact in some well studied problems. I will emphasize algorithmic and complexity issues as well as current research challenges.
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