Fast Game Content Adaptation Through Bayesian-based Player Modelling

TL;DR

This paper introduces Fast Bayesian Content Adaption (FBCA), a domain-agnostic system for dynamic difficulty adjustment in games that quickly personalizes content to player skill levels while maintaining a user model.

Contribution

The paper presents a novel Bayesian-based framework for rapid, domain-agnostic game content adaptation that can target specific difficulty levels and outperform simpler heuristics.

Findings

Successfully adapted Sudoku and Roguelike game content within 5 and 15 iterations.

Outperformed simpler DDA heuristics in personalization accuracy.

Maintained a user model for improved content targeting.

Abstract

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.