@inproceedings{ca03760230074045bd8a9f4624be12c9,
title = "AI-assisted analysis of player strategy across level progressions in a puzzle game",
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 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 play tests. 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.",
keywords = "AI-assistance, Level progression, Player strategy, Puzzle game",
author = "Britton Horn and Hoover, {Amy K.} and Yetunde Folajimi and Jackie Barnes and Casper Harteveld and Gillian Smith",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 12th International Conference on the Foundations of Digital Games, FDG 2017 ; Conference date: 14-08-2017 Through 17-08-2017",
year = "2017",
month = aug,
day = "14",
doi = "10.1145/3102071.3102083",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
editor = "Alessandro Canossa and Miguel Sicart and Casper Harteveld and Jichen Zhu and Sebastian Deterding",
booktitle = "Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017",
}