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General game playing artificial intelligence has recently seen importantadvances due to the various techniques known as 'deep learning'. However theadvances conceal equally important limitations in their reliance on: massivedata sets; fortuitously constructed problems; and absence of any human-levelcomplexity, including other human opponents. On the other hand, deep learningsystems which do beat human champions, such as in Go, do not generalise well.The power of deep learning simultaneously exposes its weakness. Given that deeplearning is mostly clever reconfigurations of well-established methods, movingbeyond the state of art calls for forward-thinking visionary solutions, notjust more of the same. I present the argument that general game playingartificial intelligence will require a generalised player model. This isbecause games are inherently human artefacts which therefore, as a class ofproblems, contain cases which require a human-style problem solving approach. Irelate this argument to the performance of state of art general game playingagents. I then describe a concept for a formal category theoretic basis to ageneralised 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 PlayerModelling using Psychology-Based Player Traits and Domain Specific Features.User Modeling and User-Adapted Interaction, 26(2), 257-306.

How to advance general game playing artificial intelligence by player modelling