  General game playing artificial intelligence has recently seen important
advances due to the various techniques known as 'deep learning'. However the
advances conceal equally important limitations in their reliance on: massive
data sets; fortuitously constructed problems; and absence of any human-level
complexity, including other human opponents. On the other hand, deep learning
systems which do beat human champions, such as in Go, do not generalise well.
The power of deep learning simultaneously exposes its weakness. Given that deep
learning is mostly clever reconfigurations of well-established methods, moving
beyond the state of art calls for forward-thinking visionary solutions, not
just more of the same. I present the argument that general game playing
artificial intelligence will require a generalised player model. This is
because games are inherently human artefacts which therefore, as a class of
problems, contain cases which require a human-style problem solving approach. I
relate this argument to the performance of state of art general game playing
agents. I then describe a concept for a formal category theoretic basis to a
generalised player model. This formal model approach integrates my existing
'Behavlets' method for psychologically-derived player modelling:
  Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player
Modelling using Psychology-Based Player Traits and Domain Specific Features.
User Modeling and User-Adapted Interaction, 26(2), 257-306.
