Teaching Summer 2026

• Models and Solvers in AI
• State models and heuristic search
• Logic, SAT solving, and ASP solving
• Classical planning: language, model, basic algorithms (heuristic search and SAT)
• Markov Decision Processes (MDPs): basic models and algorithms (VI, PI, RTDP, MCTS)
• Deep learning (DL) as model and solver; supervised learning, Approx VI, PI, MPI with DL
• Reinforcement Learning (RL): Value-based and policy gradient methods
• Current research in planning and RL

Students will present papers from a list of important recent works in machine learning and reasoning compiled by the professor. Topics include deep and reinforcement learning, transformer and GNN architectures, and more. The focus will be in the use of these techniques in simple applications that are crisp and easy to understand. The list of papers will also include those underlying well-known learning systems like as Dalle-E, ChatGPT, Alphazero, Gato, etc.

How do we make robots solve complex tasks autonomously? This lab course explores the algorithmic foundations of intelligent robotics. Students will build a full autonomy stack in the MuJoCo simulation environment, treating navigation, manipulation, and task planning as fundamental search and learning problems.