Dynamic Difficulty Adjustment via Fast User Adaptation

Hee Seung Moon, Jiwon Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationUIST 2020 - Adjunct Publication of the 33rd Annual ACM Symposium on User Interface Software and Technology
PublisherAssociation for Computing Machinery, Inc
Pages13-15
Number of pages3
ISBN (Electronic)9781450375153
DOIs
Publication statusPublished - 2020 Oct 20
Event33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020 - Virtual, Online, United States
Duration: 2020 Oct 202020 Oct 23

Publication series

NameUIST 2020 - Adjunct Publication of the 33rd Annual ACM Symposium on User Interface Software and Technology

Conference

Conference33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020
CountryUnited States
CityVirtual, Online
Period20/10/2020/10/23

Bibliographical note

Publisher Copyright:
© 2020 Owner/Author.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Software

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