16.420 Planning Under Uncertainty
Concepts, principles, and methods for planning with imperfect knowledge. Topics include state estimation, planning in information space, partially observable Markov decision processes, reinforcement learning and planning with uncertain models. Students will develop an understanding of how different planning algorithms and solutions techniques are useful in different problem domains. Previous coursework in artificial intelligence and state estimation strongly recommended.
This class has 16.413 as a prerequisite.
This class counts for a total of 12 credits. This is a graduate-level class.
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