# Advanced Programming in Computational Intelligence in Games ##### Summer ##### (in German: Advanced Programming in Computational Intelligence in Games )
##### **Module-ID:** **FIN-INF-120495**
**Abbreviation** AP-CIG **Credit Points** 6 **Semester** Summer **Term** starting at 1. **Duration** 1 Semester **Language** english **Level** Master
**Link:**[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Advanced+Programming+in+Computational+Intelligence+in+Games&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Advanced Programming in Computational Intelligence in Games")
**Limited accessability:**
**Responsibility:**Sanaz Mostaghim
**Lecturer:**Sanaz Mostaghim
**Classes:**- Lecture *Advanced Programming in Computational Intelligence in Games* - Tutorials *Advanced Programming in Computational Intelligence in Games*
**Applicability in curriculum:**- M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. VC: Computer Science
**Intended learning outcomes:** Students ... - carry out a large programming project using the concepts from the theoretical part of the course. - have a full understanding of the algorithms of computational Intelligence in Games. - learn to develop new ideas and apply them to new problems in the context of AI and Games. - learn to program the methodologies from the lectures in teams. **Content:** This course addresses the basic and advanced topics in the area of computational intelligence and games and contains three parts: Part 1 addresses the basics in Evolutionary Game Theory (EGT). In this part you will learn about simple games such as scissors/rock/paper and the focus on the strategies for playing games. Part 2 is about learning agents, and we focus on reinforcement learning mechanisms. There are three questions for games: - How can we use the information from a search mechanism to learn? - How can we use reinforcement learning to find for a better strategy? - How can we use reinforcement learning as a search mechanism? The application is on board games. Part 3 contains the advanced topics in games and artificial intelligence How can we consider physical constraints of a spaceship while moving in an unknown terrain. Other topics are Monte Carlo Tree Search, Procedural Content Generation, Decision Making and Multi-Objective Learning. **Workload:** 56 contact hours + 94h self study
**Pre-examination requirements:****Type of examination:****Teaching method / lecture hours per week (SWS):**
- Hausarbeit (home work containing programming) - Exercises and Tutorials - Klausur (written Exam) 120 Minuten - lectures (2 SWS) - exercise classes (2 SWS)
**Prerequisites according to examination regulations:****Recommended prerequisites:**
none Programming experience
**Media:****Literature:**
- ­Ian Millington and John Funge, Artificial Intelligence for Games, CRC Press, 2009 - Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998 - Jorgen W. Weibull, Evolutionary Game Theory, MIT Press, 1997 - Thomas Vincent, Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics, Cambridge University Press, 2005 - Josef Hofbauer, Karl Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, 1998
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