Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 2024 IEEE Conference on Games, CoG 2024 |
| Herausgeber (Verlag) | IEEE Computer Society |
| ISBN (elektronisch) | 9798350350678 |
| ISBN (Print) | 979-8-3503-5068-5 |
| Publikationsstatus | Veröffentlicht - 5 Aug. 2024 |
| Veranstaltung | 6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italien Dauer: 5 Aug. 2024 → 8 Aug. 2024 |
Publikationsreihe
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| ISSN (Print) | 2325-4270 |
| ISSN (elektronisch) | 2325-4289 |
Abstract
Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics [1], [2]. Decision-making problems in real-world sports share many similarities to such domains. Nevertheless, not many frameworks on sports games exist. In this paper, we present the tennis match simulation environment Match Point AI, in which different agents can compete against real-world data-driven bot strategies. Next to presenting the framework, we highlight its capabilities by illustrating, how MCTS can be used in Match Point AI to optimize the shot direction selection problem in tennis. While the framework will be extended in the future, first experiments already reveal that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Software
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- BibTex
- RIS
Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Match Point AI
T2 - 6th Annual IEEE Conference on Games, CoG 2024
AU - Nübel, Carlo
AU - Dockhorn, Alexander
AU - Mostaghim, Sanaz
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/8/5
Y1 - 2024/8/5
N2 - Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics [1], [2]. Decision-making problems in real-world sports share many similarities to such domains. Nevertheless, not many frameworks on sports games exist. In this paper, we present the tennis match simulation environment Match Point AI, in which different agents can compete against real-world data-driven bot strategies. Next to presenting the framework, we highlight its capabilities by illustrating, how MCTS can be used in Match Point AI to optimize the shot direction selection problem in tennis. While the framework will be extended in the future, first experiments already reveal that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.
AB - Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics [1], [2]. Decision-making problems in real-world sports share many similarities to such domains. Nevertheless, not many frameworks on sports games exist. In this paper, we present the tennis match simulation environment Match Point AI, in which different agents can compete against real-world data-driven bot strategies. Next to presenting the framework, we highlight its capabilities by illustrating, how MCTS can be used in Match Point AI to optimize the shot direction selection problem in tennis. While the framework will be extended in the future, first experiments already reveal that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.
KW - Monte Carlo Tree Search
KW - Sports Analysis
KW - Tennis
UR - http://www.scopus.com/inward/record.url?scp=85203517927&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.05960
DO - 10.48550/arXiv.2408.05960
M3 - Conference contribution
AN - SCOPUS:85203517927
SN - 979-8-3503-5068-5
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - Proceedings of the 2024 IEEE Conference on Games, CoG 2024
PB - IEEE Computer Society
Y2 - 5 August 2024 through 8 August 2024
ER -