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Artificial Intelligence for Games (SS 2023)


  • Course: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Matthias Schubert
  • Assistant: Niklas Strauss, Zongyue Li
  • Beneficial: Lecture "Knowledge Discovery in Databases I" or "Machine Learning" or other data analytics methods
  • Audience: The course is directed towards master students in informatics and media informatics
  • Further Information: Moodle-Page

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Tue, 13,00 - 16,00 h Geschw.-Scholl-Pl. 1 (F) -  F 007 18.04.2023
Tutorial 1 Wed, 14,00 - 16,00 h Geschw.-Scholl-Pl. 1 (M) - M 209 26.04.2023
Tutorial 2 Wed, 16,00 - 18,00 h Geschw.-Scholl-Pl. 1 (M) - M 209 26.04.2023


Computer Games and Games related formats are an essential branch of the media industry with sales exceeding those of the music or the movie industry. In many games, it is necessary to build up a dynamic environment with autonomously acting entities. This comprises any types of mobile objects, non-player characters, computer opponents or the dynamics of the environment itself. To model these elements, techniques from the area of Artificial Intelligence allow for modelling adaptive environments with interesting dynamics. From the point of view of AI Research, games currently provide multiple environments which allow to develop breakthrough technology in Artificial Intelligence and Deep Learning. Projects like OpenAIGym, AlphaGo, OpenAI5 or Alpha-Star earned a lot of attention in the AI research community as well as in the broad public. The reason for the importance of games for developing autonomous systems is that games provide environments usually allowing fast throughputs and provide clearly defined tasks for a learning agent to accomplish. The lecture provides an overview of techniques for building up environment engines and making these suitable for largescale, high-throughput games and simulations. Furthermore, we will discuss the foundations of modelling agent behaviour and how to evaluate it in deterministic and non-deterministic settings. Based on this formalisms, we will discuss how to analyse and predict agent or player behaviour. Finally, we will introduce various techniques for optimizing agent behaviour such as sequential planning and reinforcement learning.