I am a PhD student in the group, with interests in AI-bots for video games and the dynamic alteration of video games for improved gameplay.
The aim of this project is to show that player behaviour in games can be meaningfully classified into subgroups, and that rule sets of the games in consideration can be adapted to improve the enjoyability for players in each considered subgroup. Predictive machine learning can then be used to classify new players during the first minutes of play and adapt the game rules to apply the rule changes learned earlier to increase enjoyability. This process can be divided into several subprojects that act as milestones during the work:
Data collection in a video game: A suitable platform for behaviour analysis has to be found and adapted. The chosen video game will be implemented / adapted for easy and efficient data collection and user polls.
Sub-grouping and Clustering of game data: The collected game data will be used to create groups of players. The measures which are most distinguishing can be used to assign attributes to the groups in order for game developers to understand the separation.
Predictive machine learning: Given the behaviour groups, incomplete data from a player can be used to predict the group in which the current player fits best. The earlier this distinction is possible, the sooner the software can be adapted to the specific group.
Iterative adaptation learning: Adapting software to a user is not straight forward. A priori it is not clear which modifications are satisfying for the user.
Generalisation of framework: Several games will be used to show that the user groups are game-independent, i.e., if two players are classified into a group for one game, it is likely that they are classified in a group for another game.