Principles of Autonomy and Decision Making

3-0-9 units · Graduate

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 sampling-based search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, Bayesian filtering, reinforcement learning, and machine learning. Includes methods for satisfiability and optimization of logical and finite domain constraints, graphical models, and methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments.