  We present the second in a series of three academic essays which deal with
the question of how to build a generalized player model. We begin with a
proposition: a general model of players requires parameters for the subjective
experience of play, including at least three areas: a) player psychology, b)
game structure, and c) actions of play. Based on this proposition, we pose
three linked research questions, which make incomplete progress toward a
generalized player model: RQ1 what is a necessary and sufficient foundation to
a general player model?; RQ2 can such a foundation improve performance of a
computational intelligence-based player model?; and RQ3 can such a player model
improve efficacy of adaptive artificial intelligence in games? We set out the
arguments for each research question in each of the three essays, presented as
three preprints. The second essay, in this preprint, illustrates how our
'Behavlets' method can improve the performance and accuracy of a predictive
player model in the well-known Pac-Man game, by providing a simple foundation
for areas a) to c) above. We then propose a plan for future work to address RQ2
by conclusively testing the Behavlets approach. This plan builds on the work
proposed in the first preprint essay to address RQ1, and in turn provides
support for work on RQ3. The Behavlets approach was described previously;
therefore if citing this work please use the correct citation: Cowley B,
Charles D. Behavlets: a Method for Practical Player Modelling using
Psychology-Based Player Traits and Domain Specific Features. User Modelling and
User-Adapted Interaction. 2016 Feb 8; online (Special Issue on Personality in
Personalized Systems):1-50.
