Type of course: Lecture

Study programs:
Master Computer Science
Master Data Science
Master Software Systems Engineering

Offering chair:  Machine Learning and Reasoning (i6), RWTH

Contents:

- 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.

Recommended prior knowledge: Bachelor degree in CS or equivalent. Basic knowledge of probability theory and logic, basic AI and ML course.

References:

 - S. Russell and P. Norvig. AI: A Modern approach, 4th edition, 2021
- R. Sutton and A. Barto. Reinforcement learning: An introduction. 2nd Edition, 2018
- H. Geffner, B. Bonet. A Concise Introduction to Models and Methods for Automated Planning. 2013

Website: https://www-i6.informatik.rwth-aachen.de/~hector.geffner/Lecture-S2023.html