Techniques for planning, learning, and decision making under uncertainty and in multi-agent environments. Topics include Markov Decision Processes (MDPs), partially observable MDPs, reinforcement learning, game theory, Bayesian networks, and special topics.

Learning Objectives:

  1. Present models and techniques used for planning and decision making, especially in uncertain environments.
  2. Study decision making in multi-agent environments.
  3. Implement systems involving state-of-the-art methods in artificial intelligence.
  4. Study reinforcement learning and statistical methods for improving the performance of intelligent agents.
  5. Present open problems in artificial intelligence.

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