Action and Planning in AI: Learning, Models, and Algorithms
Course type
Lecture
Study programs
- Master Computer Science
- Master Data Science
- Master Software Systems Engineering
Content
- 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
Objectives
Knowledge: On completion, the students will have acquired detailed knowledge of
- models and algorithms for planning,
- models and algorithms for reinforcement learning, and
- models and algorithms for reinforcement learning that use deep neural networks.
Skills: On completion, the students will know
- how these various algorithms work,
- when they can be applied, and
- how they can be applied.
Competences: On completion, the students will be able to
- model and solve sequential decision problems,
- formulate and solve sequential decision problems by trial and error when the model is not known,
- formulate and solve sequential decision problems over large state spaces using deep nets.
Recommended prior knowledge
Basic knowledge of probability theory and logic, basic AI and ML courses.
Recommended reading
- 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
Questions?
If you have any questions, please contact Jonas Reiher.