A Demonstration of KiaiTime: A Mixed-Initiative PCGML Rhythm Game Editor

Halina, Emily; Guzdial, Matthew

doi:10.1609/aiide.v17i1.18916

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Kesimpulan dari proyeknya adalah "Seaboard" memiliki efek positif pada pengalaman para pemain dari SeaRhythm. Penelitian lain yang dilakukan oleh [10] berhasil menghadirkan editor PCGML yang merupakan campuran Rhythm Game Taiko no Tatsujin yang memberikan inspirasi dan guidance bagi penggunanya. Demonstrasi ini menampilkan UI dan model interaksi KiaiTime, memberikan wawasan tentang pengembangan game.…”

Section: Pendahuluanunclassified

Dancer vs Non Dancer: Korelasi Kontroler Terhadap Game Experience Pada Rhythm Game

Ni'maturahmah

Rizky

2022

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Dalam penelitian ini, penulis akan meneliti tentang korelasi kontroler terhadap game experience pada rhythm game dimana untuk mengetahui lebih efektif penggunaan kontroler kamera ipad atau nintendo switch. Dengan menggunakan metode CEGE atau kuisioner yang terbagi atas beberapa bagian yaitu enjoyment, frustration, Puppetry, dan bagian Video-Game yang diberikan terhadap 22 responden dengan 38 pertanyaan. Hasil dari penelitian didapat bahwa untuk pemain pemula atau non-dancer akan lebih nyaman menggunakan kamera ipad, sedangkan pemain dancer atau yang ahli lebih nyaman menggunakan joycon. Dengan adanya penelitian ini diharapkan dapat menjadi acuan bagi penelitian yang serupa.

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Section: Pendahuluanunclassified

Dancer vs Non Dancer: Korelasi Kontroler Terhadap Game Experience Pada Rhythm Game

Ni'maturahmah

Rizky

2022

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“…There has been prior research into automated chart creation and procedural content generation via machine learning approaches(PCGML) applications in Taiko no Tatsujin [2,3]. KiaiTime, as an example, is a system that includes mixed-initiative co-creative functions with a selection of difficulties.…”

Section: Introductionmentioning

confidence: 99%

Maimai Chart Generation Model Based on Machine Learning Algorithms

Lin

2023

ACE

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Chart generation for rhythm games is a demanding task that requires specialized design knowledge. In the past, there were several models proposed to generate charts by AI for different rhythm games. However, no full model has yet been designed for Maimai DX. In this paper, the author presents a model with high modularity. The main research approach is based on prior models and fits existing algorithms into the model presented by the author. The main purpose of this paper is to propose a possible chart generation model for Maimai DX and to propose improvements for future iterations. The model is designed by blocks, and each block is composed of specific goals and algorithms. The design is characterized by a high degree of modularity in the model, which provides space for future iterations because parts can be easily replaced.

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A Demonstration of KiaiTime: A Mixed-Initiative PCGML Rhythm Game Editor

Cited by 2 publications

References 10 publications

Dancer vs Non Dancer: Korelasi Kontroler Terhadap Game Experience Pada Rhythm Game

Dancer vs Non Dancer: Korelasi Kontroler Terhadap Game Experience Pada Rhythm Game

Maimai Chart Generation Model Based on Machine Learning Algorithms

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