Effective immediately, we are looking for one student helper (HiWi) supporting us in correcting/grading student solutions for the two-weekly assignments and the organization and grading of the exams for the course "Introduction to Introduction to Artificial Intelligence (WS 2019/2020)".
The Cluster of Excellence "Internet of Production" is a huge interdisciplinary project with more than 25 institutes at the RWTH. The KBSG is part of that cluster and is involved in decision support for production processes. That includes modelling and planning of processes, and using AI techniques to optimise these processes.
Recent work in the field of game AI has been leaning towards creating agents able to play games and compete with human professionals. There exists several approaches to build such agents. Whether they are built by hand or by hard coding or using some learning techniques such as Reinforcement Learning (RL) or Genetic Programming (GP). However, Such approaches are based purely on observations.
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.
- To access the L2P room, you need to register for the course in Campus ("Zum klassischen Anmeldeverfahren"). There we publish slides, announcements, etc.
- To take the exam, you need to do the modular registration process which closes 10 November.