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Towards a generalized player model through the PEAS framework

Authors:

Omid Mohaddesi,

Casper HarteveldAuthors Info & Claims

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

Article No.: 98, Pages 1 - 7

https://doi.org/10.1145/3337722.3341856

Published: 26 August 2019 Publication History

Abstract

This paper presents steps towards a generalized player model built around an external personalization and design framework. The external framework, the Player, Environment, Agents, System (PEAS) framework, resulted from a broad scope review of the personalization, player modeling, and game design literature. Leveraging this framework allows us to define a mapping from existing player and personality models to a uniform representation of player preferences over game components. We present our pipeline for developing a generalized player model from these existing models, how to translate and blend them, and finally how to use the blended model for recommending and personalizing games. We follow up the presentation of our pipeline with an extended example to highlight how two existing player modeling approaches can be combined into a singular model, and how that blended model can be used.

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Cited By

Marcin Wolański Robert Karol Jędrasiak (2024)A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR SimulatorsSAFETY & FIRE TECHNOLOGY10.12845/sft.64.2.2024.464:2(56-65)Online publication date: 10-Dec-2024
https://doi.org/10.12845/sft.64.2.2024.4
Tyack AMekler E(2024)Self-Determination Theory and HCI Games Research: Unfulfilled Promises and Unquestioned ParadigmsACM Transactions on Computer-Human Interaction10.1145/367323031:3(1-74)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673230
Shah JThue D(2023)Representing Player Behaviour via Graph Embedding Techniques: A Case Study in Dota 22023 IEEE Conference on Games (CoG)10.1109/CoG57401.2023.10333216(1-8)Online publication date: 21-Aug-2023
https://doi.org/10.1109/CoG57401.2023.10333216
Show More Cited By

Index Terms

Towards a generalized player model through the PEAS framework
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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Published In

cover image ACM Other conferences

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

Copyright © 2019 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2019

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FDG '19

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

August 26 - 30, 2019

California, San Luis Obispo, USA

Acceptance Rates

FDG '19 Paper Acceptance Rate 46 of 124 submissions, 37%;

Overall Acceptance Rate 152 of 415 submissions, 37%

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Cited By

Marcin Wolański Robert Karol Jędrasiak (2024)A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR SimulatorsSAFETY & FIRE TECHNOLOGY10.12845/sft.64.2.2024.464:2(56-65)Online publication date: 10-Dec-2024
https://doi.org/10.12845/sft.64.2.2024.4
Tyack AMekler E(2024)Self-Determination Theory and HCI Games Research: Unfulfilled Promises and Unquestioned ParadigmsACM Transactions on Computer-Human Interaction10.1145/367323031:3(1-74)Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1145/3673230
Shah JThue D(2023)Representing Player Behaviour via Graph Embedding Techniques: A Case Study in Dota 22023 IEEE Conference on Games (CoG)10.1109/CoG57401.2023.10333216(1-8)Online publication date: 21-Aug-2023
https://doi.org/10.1109/CoG57401.2023.10333216
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
https://doi.org/10.1080/10447318.2021.2020008
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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