  This paper focuses on building personalized player models solely from player
behavior in the context of adaptive games. We present two main contributions:
The first is a novel approach to player modeling based on multi-armed bandits
(MABs). This approach addresses, at the same time and in a principled way, both
the problem of collecting data to model the characteristics of interest for the
current player and the problem of adapting the interactive experience based on
this model. Second, we present an approach to evaluating and fine-tuning these
algorithms prior to generating data in a user study. This is an important
problem, because conducting user studies is an expensive and labor-intensive
process; therefore, an ability to evaluate the algorithms beforehand can save a
significant amount of resources. We evaluate our approach in the context of
modeling players' social comparison orientation (SCO) and present empirical
results from both simulations and real players.
