16.410 Principles of Autonomy and Decision Making
Surveys decision making methods used to create highly autonomous systems and decision aids. Applies models, principles and algorithms taken from artificial intelligence and operations research. Focuses on planning as state-space search, including uninformed, informed and stochastic search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, and Bayesian filtering. Also emphasizes planning with real-world constraints using constraint programming. Includes methods for satisfiability and optimization of logical, temporal and finite domain constraints, graphical models, and linear and integer programs, as well as methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments.
16.410 will be offered this semester (Fall 2018). It is instructed by B. C. Williams.
Lecture occurs 9:30 AM to 11:00 AM on Mondays and Wednesdays in 9-354.
This class counts for a total of 12 credits.
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