The primary goal of this workshop is to bring researchers working on various aspects of machine learning and games together. We want to provide a venue for discussing future directions for machine learning in games, both for academia and the industry.
The intention is to keep the scope of the workshop rather broad and include topics such as:
- Learning how to play games well, for games ranging from
deterministic and discrete boardgames to non-deterministic,
continuous, real time, action oriented games.
- Player/opponent/team modeling, for goals such as improving
artificial players in competitive games, mimicing human players, or game or learning curve adaptation.
- Game analysis, for automatic skillranking, matchmaking, or player and team behavior analysis (fraud detection) in multiplayer games.
- Automated content or story generation for games, possibly with attention to user specific constraints and preferences.
- Game adaptivity, e.g. for raising or lowering difficulty levels
dependent on the players proficiency, avoiding the emergence of player routines that are guaranteed to beat the game. This topic also includes concerns on game stability and performance guarantees for artificial opponents.
- Novel learning scenarios arising from practical problems in games.
- Machine learning perspectives in/from the games industry
- Christian Thurau, Fraunhofer IAIS and B-IT, University of Bonn
- Kurt Driessens, Katholieke Universiteit Leuven
- Olana Missura, Fraunhofer IAIS and University of Bonn