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AI-assisted analysis of player strategy across level progressions in a puzzle game

Published: 14 August 2017 Publication History

Abstract

Presenting levels commensurate with players' current understanding of game mechanics and level design is a significant challenge in designing games. Often game designers create levels by hand intending for the levels to increase in difficulty over the the course of the game while relying on their intuition or extensive user feedback, reiteration, and testing. Instead, this study starts from a number of procedurally generated levels originally generated by parameters expected to encourage a good difficulty progression and then presented to players during playtests. A number of AI-bots with different characteristics were then designed to assess the difficulty of each level. These findings are then compared with player data. Our findings show that bots encapsulating idealized player strategies can help us create a richer model of level difficulty that then reveals useful information about player struggles and learning across level progressions.

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Cited By

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  • (2021)Game Mechanic Alignment TheoryProceedings of the 16th International Conference on the Foundations of Digital Games10.1145/3472538.3472571(1-11)Online publication date: 3-Aug-2021
  • (2021)Uprising E-sports Industry: machine learning/AI improve in-game performance using deep reinforcement learning2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)10.1109/MLISE54096.2021.00112(547-552)Online publication date: Jul-2021
  • (2020)Understanding Player Patterns by Combining Knowledge-Based Data Abstraction with Interactive VisualizationProceedings of the Annual Symposium on Computer-Human Interaction in Play10.1145/3410404.3414257(254-266)Online publication date: 2-Nov-2020
  • Show More Cited By

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cover image ACM Other conferences
FDG '17: Proceedings of the 12th International Conference on the Foundations of Digital Games
August 2017
545 pages
ISBN:9781450353199
DOI:10.1145/3102071
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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  • Microsoft: Microsoft
  • Massive Entertainment: Massive Entertainment

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2017

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Author Tags

  1. AI-assistance
  2. level progression
  3. player strategy
  4. puzzle game

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  • Research-article

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FDG'17
Sponsor:
  • Microsoft
  • Massive Entertainment

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FDG '17 Paper Acceptance Rate 36 of 89 submissions, 40%;
Overall Acceptance Rate 152 of 415 submissions, 37%

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Cited By

View all
  • (2021)Game Mechanic Alignment TheoryProceedings of the 16th International Conference on the Foundations of Digital Games10.1145/3472538.3472571(1-11)Online publication date: 3-Aug-2021
  • (2021)Uprising E-sports Industry: machine learning/AI improve in-game performance using deep reinforcement learning2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)10.1109/MLISE54096.2021.00112(547-552)Online publication date: Jul-2021
  • (2020)Understanding Player Patterns by Combining Knowledge-Based Data Abstraction with Interactive VisualizationProceedings of the Annual Symposium on Computer-Human Interaction in Play10.1145/3410404.3414257(254-266)Online publication date: 2-Nov-2020
  • (2018)Exploring the hearthstone deck spaceProceedings of the 13th International Conference on the Foundations of Digital Games10.1145/3235765.3235791(1-10)Online publication date: 7-Aug-2018

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