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*) Situation Calculus is a dialog of the first order logic for representing chances in a dynamic world in a formal way. All changes are the results of actions. The "state" of the world is represented by situations. The key feature of the SC is that it is based on logic and therefore all tools for logic can be use. Therefore, SC allows to reason about actions, change and situation, to make proofs or to plan. The SC is not only a theoretical construct. There exist implementation like the program language Golog which can be used to program and control real systems like robots.

*) Game theory is a rather classical topic in mathematics and computer
science. We will discuss the theory about numbers and nimbers and see
how to (optimal) play the chocolate game, NIM, Kayles and other
classical known (and unknown) games.

*) Artificial Intelligence (AI): AI is one large and active research
area in computer science. Building intelligent systems that interact
with other intelligent entities are of particular interest. In my part
of the lecture Ill talk about special aspects of knowledge
representation and reasoning. In particular Ill focus on
non-monotonic reasoning and its application to diagnosis. The
objectives of the talk are (1) to get an impression about the state of
the art in AI, (2) to get knowledge about open research issues, and
(3) to understand how to represent and use knowledge to fulfill an
intelligent task, i.e., fault detection and localization.

*) Functional Programming: In this part of the course we will introduce Functional Programming using the programming language Scala. We cover different forms of recursion, abstraction with higher-order functions, the combination of OO with functional programming, reasoning over functional programs, and concurrent/distributed programming in the Actor model.

*) Constraint Programming (CP): CP is “one of the closest approaches computer science has yet made to the Holy Grail of programming: the user states the problem, the computer solves it.” [Freuder 1997]
CP is used in many application areas such as configuration or scheduling. In this part of the course we will discuss major approaches to represent and solve constraint satisfaction problems (CSPs). The major goals are (1) to get an understanding of the basic formal properties of CSPs and (2) to be able to represent real-world problems as a corresponding CSP. [E.C. Freuder. In Pursuit of the Holy Grail. Constraints, 2(1): 57-61, 1997.]