How to advance general game playing artificial intelligence by player modelling

TL;DR

This paper argues that advancing general game playing AI requires developing a human-inspired, formalized player model, integrating psychological traits to overcome deep learning limitations and better handle human-like complexity.

Contribution

It introduces a formal category theoretic framework for a generalized player model, building on the 'Behavlets' method for psychologically-informed player modeling.

Findings

Deep learning systems lack generalization to human-like complexity.

A formal model can integrate psychological traits for better human-like AI.

Proposes a category theoretic basis for a generalized player model.

Abstract

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.