Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914929675599872 |
|---|---|
| author | Lin, Chiu-Chou Shih, Yu-Wei Kuo, Kuei-Ting Chen, Yu-Cheng Chen, Chien-Hua Chiu, Wei-Chen Wu, I-Chen |
| author_facet | Lin, Chiu-Chou Shih, Yu-Wei Kuo, Kuei-Ting Chen, Yu-Cheng Chen, Chien-Hua Chiu, Wei-Chen Wu, I-Chen |
| contents | How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such as hero combinations in multiplayer online battle arena (MOBA) games or decks in card games-is essential for enhancing gameplay and achieving balance. We have developed two advanced measures that extend beyond the simplistic win rate to quantify balance in zero-sum competitive scenarios. These measures are derived from win value estimations, which employ strength rating approximations via the Bradley-Terry model and counter relationship approximations via vector quantization, significantly reducing the computational complexity associated with traditional win value estimations. Throughout the learning process of these models, we identify useful categories of compositions and pinpoint their counter relationships, aligning with the experiences of human players without requiring specific game knowledge. Our methodology hinges on a simple technique to enhance codebook utilization in discrete representation with a deterministic vector quantization process for an extremely small state space. Our framework has been validated in popular online games, including Age of Empires II, Hearthstone, Brawl Stars, and League of Legends. The accuracy of the observed strength relations in these games is comparable to traditional pairwise win value predictions, while also offering a more manageable complexity for analysis. Ultimately, our findings contribute to a deeper understanding of PvP game dynamics and present a methodology that significantly improves game balance evaluation and design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis Lin, Chiu-Chou Shih, Yu-Wei Kuo, Kuei-Ting Chen, Yu-Cheng Chen, Chien-Hua Chiu, Wei-Chen Wu, I-Chen Artificial Intelligence Computer Science and Game Theory Information Retrieval Machine Learning Multiagent Systems How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such as hero combinations in multiplayer online battle arena (MOBA) games or decks in card games-is essential for enhancing gameplay and achieving balance. We have developed two advanced measures that extend beyond the simplistic win rate to quantify balance in zero-sum competitive scenarios. These measures are derived from win value estimations, which employ strength rating approximations via the Bradley-Terry model and counter relationship approximations via vector quantization, significantly reducing the computational complexity associated with traditional win value estimations. Throughout the learning process of these models, we identify useful categories of compositions and pinpoint their counter relationships, aligning with the experiences of human players without requiring specific game knowledge. Our methodology hinges on a simple technique to enhance codebook utilization in discrete representation with a deterministic vector quantization process for an extremely small state space. Our framework has been validated in popular online games, including Age of Empires II, Hearthstone, Brawl Stars, and League of Legends. The accuracy of the observed strength relations in these games is comparable to traditional pairwise win value predictions, while also offering a more manageable complexity for analysis. Ultimately, our findings contribute to a deeper understanding of PvP game dynamics and present a methodology that significantly improves game balance evaluation and design. |
| title | Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis |
| topic | Artificial Intelligence Computer Science and Game Theory Information Retrieval Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2408.17180 |