  In games, as well as many user-facing systems, adapting content to users'
preferences and experience is an important challenge. This paper explores a
novel method to realize this goal in the context of dynamic difficulty
adjustment (DDA). Here the aim is to constantly adapt the content of a game to
the skill level of the player, keeping them engaged by avoiding states that are
either too difficult or too easy. Current systems for DDA rely on expensive
data mining, or on hand-crafted rules designed for particular domains, and
usually adapts to keep players in the flow, leaving no room for the designer to
present content that is purposefully easy or difficult. This paper presents
Fast Bayesian Content Adaption (FBCA), a system for DDA that is agnostic to the
domain and that can target particular difficulties. We deploy this framework in
two different domains: the puzzle game Sudoku, and a simple Roguelike game. By
modifying the acquisition function's optimization, we are reliably able to
present a content with a bespoke difficulty for players with different skill
levels in less than five iterations for Sudoku and fifteen iterations for the
simple Roguelike. Our method significantly outperforms simpler DDA heuristics
with the added benefit of maintaining a model of the user. These results point
towards a promising alternative for content adaption in a variety of different
domains.

Artificial Intelligence;Applications