Fast Game Content Adaptation Through Bayesian-based Player Modelling

Miguel, González-Duque,; Palm, Rasmus; Risi, Sebastian

doi:10.1109/cog52621.2021.9619018

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…On the realm of adapting to real, particular players, González-Duque et al [16] present "Fast Bayesian Content Adaption", where Bayesian optimization is used for dynamic difficulty adaption (DDA). Their system works online, and will after each level try to adapt it to the player, based on the time they use to solve the level, to approach a target time.…”

Section: Adaptation To Players and Personasmentioning

confidence: 99%

Adapting Procedural Content Generation to Player Personas Through Evolution

Fernandes¹,

Jørgensen²,

Poldervaart³

2021

Preprint

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Automatically adapting game content to players opens new doors for game development. In this paper we propose an architecture using persona agents and experience metrics, which enables evolving procedurally generated levels tailored for particular player personas. Using our game, "Grave Rave", we demonstrate that this approach successfully adapts to four rulebased persona agents over three different experience metrics. Furthermore, the adaptation is shown to be specific in nature, meaning that the levels are persona-conscious, and not just general optimizations with regard to the selected metric.

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Section: Adaptation To Players and Personasmentioning

confidence: 99%

Adapting Procedural Content Generation to Player Personas Through Evolution

Fernandes¹,

Jørgensen²,

Poldervaart³

2021

Preprint

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“…Furthermore, the combination of Machine Learning (ML) with PCG has led to the rise of Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content by models that have been trained on existing game content [101]. PCGML has been used for autonomous content generation [177], content repair [178], mixed-initiative design [164], or content adaptation [179]. The use of user models is essential for the generation of adaptive and tailored content, and when discussed in the context of PCG, usually relates to experience-driven PCG [50].…”

Section: Modeling Players and Designersmentioning

confidence: 99%

“…Content adaptation can take place as players play or use the content online or offline, building models from collected data. For instance, Duque et al adapt and adjust the difficulty of generated content as players play the game using bayesian optimization [179]. Summerville et al model players automatically and implicitly by learning from video traces; generating levels that correspond to the latent player models [180].…”

Section: Modeling Players and Designersmentioning

confidence: 99%

E X P Loring Game De S Ign Through Human-Ai Col Laborat Ion

Alvarez¹

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Game design is a hard and multi-faceted task that intertwines different gameplay mechanics, audio, level, graphic, and narrative facets. Games' facets are developed in conjunction with others with a common goal that makes games coherent and interesting. These combinations result in plenty of games in diverse genres, which usually require a collaboration of a diverse group of designers. Collaborators can take different roles and support each other with their strengths resulting in games with unique characteristics. The multi-faceted nature of games and their collaborative properties and requirements make it an exciting task to use Artificial Intelligence (AI). The generation of these facets together requires a holistic approach, which is one of the most challenging tasks within computational creativity. Given the collaborative aspect of games, this thesis approaches their generation through Human-AI collaboration, specifically using a mixed-initiative co-creative (MI-CC) paradigm. This paradigm creates an interactive and collaborative scenario that leverages AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges, such as Human and AI goal alignment or competing properties. In this thesis, game design and the generation of game facets by themselves and intertwined are explored through Human-AI collaboration. The AI takes a colleague's role with the designer, arising multiple dynamics, challenges, and opportunities. The main hypothesis is that AI can be incorporated into systems as a collaborator, enhancing design tools, fostering human creativity, and reducing workload. The challenges and opportunities that arise from this are explored, discussed, and approached throughout the thesis. As a result, multiple approaches and methods such as quality-diversity algorithms and designer modeling are proposed to generate game facets in tandem with humans, create a better workflow, enhance the interaction, and establish adaptive experiences.

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Toward Dynamic Difficulty Adjustment with Audio Cues by Gaussian Process Regression in a First-Person Shooter

Wira

Thawonmas

2022

Entertainment Computing – ICEC 2022

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Fast Game Content Adaptation Through Bayesian-based Player Modelling

Cited by 10 publications

References 15 publications

Adapting Procedural Content Generation to Player Personas Through Evolution

Adapting Procedural Content Generation to Player Personas Through Evolution

E X P Loring Game De S Ign Through Human-Ai Col Laborat Ion

Toward Dynamic Difficulty Adjustment with Audio Cues by Gaussian Process Regression in a First-Person Shooter

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