1. Students understand the fundamental setting of strategy games.
2. Students understand the fundamental algorithms of AI game engines.
3. Students have experience in the implementation of the Minimax algorithms and related heuristics.
4. Students have experience in the implementation of Monte Carlo Tree Sampling and related heuristics (UCB1, RAVE, etc.).

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

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

Teamwork: Implementation of a game AI in Java

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