I'm sure you've heard by now, that artificial intelligence is coming to Human Resources. Of course, the manifold marketing materials and click baiting content dedicated to this growing, uh, phenomenon are predicated on the assumption that there was actual intelligence in the HR function to begin with. This point can probably be debated, which brings us to the larger question: why are we talking about AI in HR, anyways? The fact of the matter is, the problems most endemic to HR, the biggest challenges facing our profession are inherently the holes in even the most sophisticated AI solutions. Whether in reality or in speculative Science Fiction (or somewhere in between, like your provider's "product roadmap"), true AI is the HR Technology equivalent of tilting at windmills.

In this paper we describe a method of modeling play styles as deviations from approximations of game theoretically rational actions. These deviations are interpreted as containing information about player skill and player decision making style. We hypothesize that this information is useful for differentiating between players and for understanding why human player behavior is attributed intentionality which we argue is a prerequisite for believability. To investigate these hypotheses we describe an experiment comparing 400 games in the Mario AI Benchmark testbed, played by humans, with equivalent games played by an approximately game theoretically rationally playing AI agent. The player actions’ deviations from the rational agent’s actions are subjected to feature extraction, and the resulting features are used to cluster play sessions into expressions of different play styles. We discuss how these styles differ, and how believable agent behavior might be approached by using these styles as an outset for a planning agent. Finally, we discuss the implications of making assumptions about rational game play and the problematic aspects of inferring player intentions from behavior.