Active Player Modelling
This work addresses the problem of improving player modelling efficiency in games, but it is incremental as it builds on existing active learning concepts without presenting new empirical results.
The paper proposes using active learning for player modelling to achieve more efficient learning, though it requires significant changes in data collection methods, and suggests that this approach could lead to games that are curious about players and select interesting configurations for them.
We argue for the use of active learning methods for player modelling. In active learning, the learning algorithm chooses where to sample the search space so as to optimise learning progress. We hypothesise that player modelling based on active learning could result in vastly more efficient learning, but will require big changes in how data is collected. Some example active player modelling scenarios are described. A particular form of active learning is also equivalent to an influential formalisation of (human and machine) curiosity, and games with active learning could therefore be seen as being curious about the player. We further hypothesise that this form of curiosity is symmetric, and therefore that games that explore their players based on the principles of active learning will turn out to select game configurations that are interesting to the player that is being explored.