Dynamic Difficulty Adjustment via Fast User Adaptation

TL;DR

This paper introduces a meta-learning based dynamic difficulty adjustment method that quickly adapts game difficulty to individual players using minimal demo data, enhancing player engagement.

Contribution

It proposes a novel fast adaptation approach for DDA that requires only limited user data, improving over existing deep learning methods.

Findings

Outperformed baseline methods in user tests

Effective with small demo datasets

Enhances personalized gaming experience

Abstract

Dynamic difficulty adjustment (DDA) is a technology that adapts a game's challenge to match the player's skill. It is a key element in game development that provides continuous motivation and immersion to the player. However, conventional DDA methods require tuning in-game parameters to generate the levels for various players. Recent DDA approaches based on deep learning can shorten the time-consuming tuning process, but require sufficient user demo data for adaptation. In this paper, we present a fast user adaptation method that can adjust the difficulty of the game for various players using only a small amount of demo data by applying a meta-learning algorithm. In the video game environment user test (n=9), our proposed DDA method outperformed a typical deep learning-based baseline method.