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.
|Title of host publication||UIST 2020 - Adjunct Publication of the 33rd Annual ACM Symposium on User Interface Software and Technology|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||3|
|Publication status||Published - 2020 Oct 20|
|Event||33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020 - Virtual, Online, United States|
Duration: 2020 Oct 20 → 2020 Oct 23
|Name||UIST 2020 - Adjunct Publication of the 33rd Annual ACM Symposium on User Interface Software and Technology|
|Conference||33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020|
|Period||20/10/20 → 20/10/23|
Bibliographical notePublisher Copyright:
© 2020 Owner/Author.
Copyright 2020 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction