This course is about the logic of knowledge bases, in two distinct but related senses. On the one hand, a knowledge base is a collection of sentences in a representation language that entails a certain picture of the world represented. On the other hand, having a knowledge base entails being in a certain state of knowledge where a number of other epistemic properties hold. One of the principal aims of this course is to develop a detailed account of the relationship between symbolic representations of knowledge and abstract states of knowledge. Students wishing to attend the course should be familiar with first-order predicate logic.
Teaching Sommersemester 2018
The proseminar will be on different (sub-)topics from artificial intelligence. We largely follow the lines of the well known textbook by Stuart Russell and Peter Norvig "Artificial Intelligence - A Modern Approach".
We implement a peer review process for this seminar. That is, every student will read some other students' term paper and provide feedback in form of a written review. This shall not only deepen your understanding of the other topics, but it also introduces you to the academic review process.
An L²P-Lernraum of the course contains more info and material (for participants only!).
In this seminar, we will study uncertainty in the context of reasoning, planning, and scheduling. For reasoning about actions, we will look into stochastic extensions of the Situation Calculus, a well-known formalism for reasoning about dynamic domains. In the classical Situation Calculus, all actions are deterministic. In this seminar, we will learn about extensions that allow non-deterministic and probabilistic actions. For planning, we will investigate probabilistic extensions to classical planning frameworks such as the Planning Domain Definition Language (PDDL) and compare them to Markov Decision Processes (MDPs). For scheduling, we will learn about mechanisms for solving scheduling problems with probabilistic task durations.