  Models of intrinsic motivation present an important means to produce sensible
behaviour in the absence of extrinsic rewards. Applications in video games are
varied, and range from intrinsically motivated general game-playing agents to
non-player characters such as companions and enemies. The information-theoretic
quantity of Empowerment is a particularly promising candidate motivation to
produce believable, generic and robust behaviour. However, while it can be used
in the absence of external reward functions that would need to be crafted and
learned, empowerment is computationally expensive. In this paper, we propose a
modified UCT tree search method to mitigate empowerment's computational
complexity in discrete and deterministic scenarios. We demonstrate how to
modify a Monte-Carlo Search Tree with UCT to realise empowerment maximisation,
and discuss three additional modifications that facilitate better sampling. We
evaluate the approach both quantitatively, by analysing how close our approach
gets to the baseline of exhaustive empowerment computation with varying amounts
of computational resources, and qualitatively, by analysing the resulting
behaviour in a Minecraft-like scenario.
