Dynamic Difficulty Adjustment via Fast User Adaptation

Hee Seung Moon, Jiwon Seo

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

1 Citation (Scopus)

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
Country/TerritoryUnited 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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