After successful completion of the course, students are able to implement and evaluate strategy games with full information. They understand the fundamental approaches of depth search and Monte Carlo tree simulation and are able to choose and implement efficient heurstics for these approaches. Since the winter term 2021 we consider games with partial information and/or chance as well.

1. Lecture block: Setting, fundamental algorithms, heuristics, applications, state of research
2. Lab course: Goals, task, introduction into the programming environment, practical hints

IMPORTANT! Due to the Corona pandemic the lecture will be replaced by a set of videos + a summary written by the students.All details can be found in the TUWEL forum.

Central topics of the course are modeling and evaluation of strategies in games with defined rules and under full information. The desired understanding is developed interactively in the lecture part and consolidated by a realistic exercise in the practical part of the course.

Pedagogic concept

• Frame of knowledge transfer with lecture block at the beginning an exam at the end of the lecture
• Exploration of lecture contents in a lab project in groups of 2-3 students
• Application of state of the art visualization and seminar methods for enabling student participation during the lecture
• Application of an open forum for knowledge exchange over groups during the lab course

ECTS Breakdown

Description                       ECTS  Hours
---------------------------------------------
Preparation                       0.04    1.0
Lecture                           0.32    8.0
Preparation of the Group Project  0.04    1.0
Group Project Work                2.60   65.0
---------------------------------------------
Total                             3.00   75.0

Students are graded based on their performance in the practical part of the course as well as on their participation in the lecture part.

VERY IMPORTANT: Please note that due to the popularity and the limited resources of this course for the successful registration an assessment test on "reinforcement learning" has to be perfomed in the e-learning environment TUWEL under the following address (please use "Self Enrollment" in TUWEL):

https://tuwel.tuwien.ac.at/mod/quiz/view.php?id=661069

• Good knowledge in software development with Java
• Fundamental knowledge in the area of artificial intelligence
• Experience in strategy game playing