Player-Centered AI for Automatic Game Personalization: Open Problems

Zhu, Jichen; Ontañón, Santiago

doi:10.1145/3402942.3402951

Cited by 19 publications

(8 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent article on the state of AI research on personalized games confirms the market need for our proposed solution. Zhu and Ontañón [3] argue that the research can benefit from more player-centred perspectives. Authors argue that computer games represent an ideal research domain for the next generation of personalized digital applications and that to reach the full potential of personalized games, a player-centred approach is necessary.…”

Section: Related Workmentioning

confidence: 99%

One Game Fits All: Personalized Content Generation in Mobile Games

Hafnar¹,

Demšar²

2022

View full text Add to dashboard Cite

Procedural content generation uses algorithmic techniques to create large amounts of new content for games and thus reduces the cost of production. However, this content generation is typically the same for all players and is not used to personalize and optimize the game for players’ characteristics. Thus, the core of our research is the improvement of procedural content generation through personalization. We plan to achieve personalization by using modern machine learning algorithms to learn the characteristics of the player. These characteristics will be then used as input parameters for procedural content generation algorithms to produce personalized content. We expect that personalized procedural content generation will have a positive effect on the user’s gameplay experience.

show abstract

Section: Related Workmentioning

confidence: 99%

One Game Fits All: Personalized Content Generation in Mobile Games

Hafnar¹,

Demšar²

2022

View full text Add to dashboard Cite

show abstract

“…Third, perhaps the most important, further research is needed on the personalization paradox [69]. We will discuss this in the following section.…”

Section: Design Implicationsmentioning

confidence: 99%

“…Personalization technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce, and entertainment [55,69]. By using artificial intelligence (AI) to tailor themselves to individual users' needs and preferences, personalized systems have shown to improve learnability [19], usability [25], and user enjoyment [52].…”

Section: Introductionmentioning

confidence: 99%

Personalization Paradox in Behavior Change Apps

Zhu

Dallal

Gray

et al. 2021

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.

show abstract

“…These challenges of content creation become particularly salient when one also aims at providing adaptive gameplay [1,2]. In this setting, question difficulty should automatically adapt to the abilities and progress of the player [3].…”

Section: Introductionmentioning

confidence: 99%

Procedural generation of problems for elementary math education

Smeets²,

Bidarra³

2021

IJSG

View full text Add to dashboard Cite

Mathematics education plays an essential role in children’s development, and there are many online applications aimed at supporting this process. However, manually creating math problems with a variety of textual and visual content is very time-consuming and expensive. This article presents a generic approach for procedural generation of mathematical problems, including their corresponding textual representations. The content generation process consists of two phases: abstract math problem generation and text generation. For the generation of abstract math problems, we propose a generic template-based method that operates across a variety of difficulty-levels and domains, including arithmetic, comparison, ordering, mathematical relationships, measurement, and geometry. Subsequently, we propose a multi-language adaptive textual content generation pipeline to realize the generated abstract math problems into semantically coherent text questions in natural language. A workflow time gain evaluation shows that the system yields an average time saving of 56%. Further, human expert evaluation of this approach indicates that the content it generates is sensible and solvable for primary school students.

show abstract

Player-Centered AI for Automatic Game Personalization: Open Problems

Cited by 19 publications

References 55 publications

One Game Fits All: Personalized Content Generation in Mobile Games

One Game Fits All: Personalized Content Generation in Mobile Games

Personalization Paradox in Behavior Change Apps

Procedural generation of problems for elementary math education

Contact Info

Product

Resources

About