Intelligent Decision Making (IDM)
Lecturers
Objectives
- to interpret and reason about requirements specified for intelligent autonomous systems
- to establish relations between changes of the context and adaptation of the behavior
- to design mechanisms and principles for decision making in autonomous and learning systems
- to analyze fundamental properties of intelligent systems, scrutinize resulting system behavior, and to conclude on possible ways to affect systems
- to design and implement algorithms for decision making and learning
- to judge on the suitability of different methods for a given problem
Contents
- Introduction into Automated Decision Making
- Properties und Tasks of Intelligent Agents
- Logics for Automated Reasoning
- Problem Solving by Search and Exploration
- Planning of Mobile Agents and Robots
- Probabilistic Reasoning
- Learning of Controller Functions from Data
- Reinforcement Learning
- Neuro-Dynamic Programming
Literature
- Lecture Material
- Russel, P. Norvig: Artificial Intelligence - A Modern Approach. Pearson, 2021.
- S. Sutton, A.G. Barto: Reinforcement Learning. The MIT Press, 2018.
- D.P. Bertsekas, J. Tsitsiklis: Neuro-Dynamic Programming. Athena Scientific, 1996.
Recommend Prerequisites
- Algebra and Analysis (as typical for Bachelor degrees)
Credits
2L + 1T, 3 Credits
(L: lecture hours per week, T: tutorial hours per week)
The course is offered in the summer semester; the examination in the winter and summer semester (in English only).
Course Number
- to be inserted -
Assignment to Course Programs
Master of Electrical Engineering
Master of Mechatronics
Open as elective course within other Master programs