Difficulty in action based challenges: success prediction, players' strategies and profiling

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

3 Citations (Scopus)

Abstract

In this paper we extend the existing metrics for estimating difficulty, directed to action games (in particular, platform videogames) and taking into account different types of challenges. Specifically, we analyse how challenges are represented in time and space and observe the players’ behaviour when facing such challenges. Accordingly, we propose a set of models to predict the probability of failure and success in different situations. This type of assessment can serve as a validation mechanism for automatic level generation algorithms and also to perform adaptive difficulty techniques. As a final point, we analyse multiple gameplay information retrieved from gaming sessions, in which 40 users performed over 10000 trials in different types of challenges. We have verified that the main hypotheses behind the proposed metrics are applicable and the estimators are valid approximations to the players’ behaviour.
Original languageEnglish
Title of host publicationAdvances in Computer Entertainment Conference Proceedings
Subtitle of host publicationProceedings of the 11th Conference on Advances in Computer Entertainment Technology
PublisherACM
Pages9:1-9:10
Number of pages10
ISBN (Electronic)978-1-4503-2945-3
DOIs
Publication statusPublished - 1 Jan 2014
EventACE - Advances in Computer Entertainment -
Duration: 1 Jan 2014 → …

Conference

ConferenceACE - Advances in Computer Entertainment
Period1/01/14 → …

Keywords

    Cite this

    Birra, F. P. R. D. S., Santos, M. J. T. P. D., & Mourato, F. (2014). Difficulty in action based challenges: success prediction, players' strategies and profiling. In Advances in Computer Entertainment Conference Proceedings: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology (pp. 9:1-9:10). [9] ACM. https://doi.org/10.1145/2663806.2663832
    Birra, Fernando Pedro Reino da Silva ; Santos, Manuel João Toscano Próspero dos ; Mourato, Fausto. / Difficulty in action based challenges: success prediction, players' strategies and profiling. Advances in Computer Entertainment Conference Proceedings: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology. ACM, 2014. pp. 9:1-9:10
    @inproceedings{7c5d16e399df41068b21dedbf5e99aaf,
    title = "Difficulty in action based challenges: success prediction, players' strategies and profiling",
    abstract = "In this paper we extend the existing metrics for estimating difficulty, directed to action games (in particular, platform videogames) and taking into account different types of challenges. Specifically, we analyse how challenges are represented in time and space and observe the players’ behaviour when facing such challenges. Accordingly, we propose a set of models to predict the probability of failure and success in different situations. This type of assessment can serve as a validation mechanism for automatic level generation algorithms and also to perform adaptive difficulty techniques. As a final point, we analyse multiple gameplay information retrieved from gaming sessions, in which 40 users performed over 10000 trials in different types of challenges. We have verified that the main hypotheses behind the proposed metrics are applicable and the estimators are valid approximations to the players’ behaviour.",
    keywords = "Difficulty measurement, human factors, platform videogames.",
    author = "Birra, {Fernando Pedro Reino da Silva} and Santos, {Manuel Jo{\~a}o Toscano Pr{\'o}spero dos} and Fausto Mourato",
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    Birra, FPRDS, Santos, MJTPD & Mourato, F 2014, Difficulty in action based challenges: success prediction, players' strategies and profiling. in Advances in Computer Entertainment Conference Proceedings: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology., 9, ACM, pp. 9:1-9:10, ACE - Advances in Computer Entertainment, 1/01/14. https://doi.org/10.1145/2663806.2663832

    Difficulty in action based challenges: success prediction, players' strategies and profiling. / Birra, Fernando Pedro Reino da Silva; Santos, Manuel João Toscano Próspero dos; Mourato, Fausto.

    Advances in Computer Entertainment Conference Proceedings: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology. ACM, 2014. p. 9:1-9:10 9.

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

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    N2 - In this paper we extend the existing metrics for estimating difficulty, directed to action games (in particular, platform videogames) and taking into account different types of challenges. Specifically, we analyse how challenges are represented in time and space and observe the players’ behaviour when facing such challenges. Accordingly, we propose a set of models to predict the probability of failure and success in different situations. This type of assessment can serve as a validation mechanism for automatic level generation algorithms and also to perform adaptive difficulty techniques. As a final point, we analyse multiple gameplay information retrieved from gaming sessions, in which 40 users performed over 10000 trials in different types of challenges. We have verified that the main hypotheses behind the proposed metrics are applicable and the estimators are valid approximations to the players’ behaviour.

    AB - In this paper we extend the existing metrics for estimating difficulty, directed to action games (in particular, platform videogames) and taking into account different types of challenges. Specifically, we analyse how challenges are represented in time and space and observe the players’ behaviour when facing such challenges. Accordingly, we propose a set of models to predict the probability of failure and success in different situations. This type of assessment can serve as a validation mechanism for automatic level generation algorithms and also to perform adaptive difficulty techniques. As a final point, we analyse multiple gameplay information retrieved from gaming sessions, in which 40 users performed over 10000 trials in different types of challenges. We have verified that the main hypotheses behind the proposed metrics are applicable and the estimators are valid approximations to the players’ behaviour.

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    Birra FPRDS, Santos MJTPD, Mourato F. Difficulty in action based challenges: success prediction, players' strategies and profiling. In Advances in Computer Entertainment Conference Proceedings: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology. ACM. 2014. p. 9:1-9:10. 9 https://doi.org/10.1145/2663806.2663832