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research-article

Like PEAS in PoDS: the player, environment, agents, system framework for the personalization of digital systems

Authors:

Omid Mohaddesi,

Guillermo Romera Rodriguez,

Christoffer Holmgård,

Casper HarteveldAuthors Info & Claims

FDG '19: Proceedings of the 14th International Conference on the Foundations of Digital Games

Article No.: 26, Pages 1 - 15

https://doi.org/10.1145/3337722.3337756

Published: 26 August 2019 Publication History

Abstract

Personalization has been explored in the context of games in many forms (e.g., dynamic difficulty adjustment, affective video games, adaptive systems, experience-driven PCG, etc.). The majority of techniques used in these fields have relied on data-driven or manual methods for identifying game components to modify for personalization. We propose a theoretical framework for identifying and categorizing low-level components of games that can be personalized. In this paper we first perform a review of game design frameworks and personalization approaches. We then systematically identify the aspects of games which have been utilized for personalization and which components have been identified in game design frameworks as building blocks of games. We synthesize the identified components into categories of game elements. We then propose PEAS, a theoretical framework for personalization through the adaptation of the Player, Environment, Agents, and System.

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Cited By

Carvalho CTeran LMota MPereira R(2024)Technologies to Support Adaptable Game Design: A Systematic Mapping StudyJournal of the Brazilian Computer Society10.5753/jbcs.2024.309030:1(69-101)Online publication date: 26-Apr-2024
https://doi.org/10.5753/jbcs.2024.3090
Bansal RGupta SShankar KArundhati (2024)Exploring Artificial Intelligence PEAS Framework for Enhanced Decision-MakingRecent Research Reviews Journal10.36548/rrrj.2024.2.0073:2(397-409)Online publication date: Dec-2024
https://doi.org/10.36548/rrrj.2024.2.007
Carvalho CTeran LMota MPereira RSantos CBento Villela MRios da Hora Rodrigues Kde Gois Ribeiro Darin T(2022)A systematic mapping study on digital game adaptation dimensionsProceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems10.1145/3554364.3559122(1-14)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3554364.3559122
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Index Terms

Like PEAS in PoDS: the player, environment, agents, system framework for the personalization of digital systems
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models

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FDG '19: Proceedings of the 14th International Conference on the Foundations of Digital Games

August 2019

822 pages

ISBN:9781450372176

DOI:10.1145/3337722

Conference Chair:
Sebastian Deterding
University of York
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General Chair:
Foaad Khosmood
California Polytechnic State University
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Program Chairs:
Johanna Pirker
Graz University of Technology
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Thomas Apperley
CEGCS Tampere University

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FDG '19: The Fourteenth International Conference on the Foundations of Digital Games

August 26 - 30, 2019

California, San Luis Obispo, USA

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Overall Acceptance Rate 152 of 415 submissions, 37%

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https://doi.org/10.5753/jbcs.2024.3090
Bansal RGupta SShankar KArundhati (2024)Exploring Artificial Intelligence PEAS Framework for Enhanced Decision-MakingRecent Research Reviews Journal10.36548/rrrj.2024.2.0073:2(397-409)Online publication date: Dec-2024
https://doi.org/10.36548/rrrj.2024.2.007
Carvalho CTeran LMota MPereira RSantos CBento Villela MRios da Hora Rodrigues Kde Gois Ribeiro Darin T(2022)A systematic mapping study on digital game adaptation dimensionsProceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems10.1145/3554364.3559122(1-14)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3554364.3559122
Mohaddesi OGriffin JErgun OKaeli DMarsella SHarteveld C(2022)To Trust or to Stockpile: Modeling Human-Simulation Interaction in Supply Chain ShortagesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502089(1-18)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502089
Zhang KBai JLiu J(2022)Generating Game Levels of Diverse Behaviour Engagement2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893697(167-174)Online publication date: 21-Aug-2022
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Paraschos PKoulouriotis D(2022)Game Difficulty Adaptation and Experience Personalization: A Literature ReviewInternational Journal of Human–Computer Interaction10.1080/10447318.2021.202000839:1(1-22)Online publication date: 18-Apr-2022
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Javvaji NHarteveld CSeif El-Nasr MMirza-Babaei PMcArthur VVanden Abeele VBirk M(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
https://dl.acm.org/doi/10.1145/3410404.3414257
Zhu JOntañón S(2020)Player-Centered AI for Automatic Game Personalization: Open ProblemsProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3402951(1-8)Online publication date: 15-Sep-2020
https://dl.acm.org/doi/10.1145/3402942.3402951
Vítek A(2020)Cross-Game Modeling of Player's Behaviour in Free-To-Play GamesProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3398677(384-387)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3340631.3398677
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