Disciplines/fields: Computer Science, Cognitive Science

Duration: 4 sessions

Course Content

Session 1:
We first try to define what Artificial Intelligence is and what kinds of problems it tries to solve. We then start with basic concepts and terminology and simple problem-solving mechanisms.

Session 2:
In this session we discuss the two most important problem-solving and representation techniques of AI: search in abstract state spaces and probabilistic reasoning. More complex AI techniques are mostly variations and extensions of search and/or probabilistic reasoning as we will see in the later sessions.

Session 3:
We discuss several mechanisms for representing knowledge about the world. We re-encounter search as the basic reasoning mechanism for symbolic knowledge and probabilistic reasoning in the form of Bayesian networks.

Session 4:
This session gives a very short introduction to AI learning, another variation of the basic AI techniques from Session 2. The second half of the session looks at complete cognitive systems that should be able to interact with humans in an intuitive way. The challenges here are not single algorithms, but rather architectural and organizational questions.


  • having an overview of the problems tackled in AI
  • understanding the basic AI techniques of search and probabilistic reasoning and how they are applied in more complex techniques such as learning
  • knowing about the challenges of constructing complete cognitive systems that interact with humans


Stuart Russell and Peter Norvig. "Artificial Intelligence: A Modern Approach", 3rd edition, 2010
Douglas R. Hofstadter. "Gödel, Escher, Bach: An Eternal Golden Braid", 1979
Harry Harrison und Marvin Minsky. "The Turing Option", 1992


Alexandra Kirsch is an assistant professor at the University of Tübingen. She received her diploma degree in Computer Science in 2003 and her doctoral degree in 2008 from Technische Universität München. After her PhD she worked as a strategy consultant before returning to TU München as a senior researcher, leading a junior research group in the cluster of excellence "Cognition for Technical Systems" (CoTeSys) and supported as a Carl-von-Linde Junior Fellow by the Institute for Advanced Study of TU München. Since 2012 she is a member of the Young Scholars Programme of the Bavarian Academy of Sciences and Humanities. Her research aims at making technical systems more understandable for users by developing and applying artificial intelligence methods. Specific research interests include human-robot collaboration, autonomous decision-making, knowledge-based failure recognition, knowledge representation, robot learning and plan-based robot control. In all these efforts findings from and collaborative work with psychology and neuroscience play an important role.