Increasing Player Performance and Game Experience in High Latency Systems
| Autor: | Valentin Schwind, David Halbhuber, Niels Henze |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer Networks and Communications Computer science business.industry Cloud gaming Deep learning Network on Real-time computing Work (physics) ComputingMilieux_PERSONALCOMPUTING Cloud computing Human-Computer Interaction Artificial intelligence Latency (engineering) business Video game Social Sciences (miscellaneous) |
| Zdroj: | Proceedings of the ACM on Human-Computer Interaction. 5:1-20 |
| ISSN: | 2573-0142 |
| DOI: | 10.1145/3474710 |
| Popis: | Cloud gaming services and remote play offer a wide range of advantages but can inherent a considerable delay between input and action also known as latency. Previous work indicates that deep learning algorithms such as artificial neural networks (ANN) are able to compensate for latency. As high latency in video games significantly reduces player performance and game experience, this work investigates if latency can be compensated using ANNs within a live first-person action game. We developed a 3D video game and coupled it with the prediction of an ANN. We trained our network on data of 24 participants who played the game in a first study. We evaluated our system in a second user study with 96 participants. To simulate latency in cloud game streaming services, we added 180 ms latency to the game by buffering user inputs. In the study we predicted latency values of 60 ms, 120 ms and 180 ms. Our results show that players achieve significantly higher scores, substantially more hits per shot and associate the game significantly stronger with a positive affect when supported by our ANN. This work illustrates that high latency systems, such as game streaming services, benefit from utilizing a predictive system. |
| Databáze: | OpenAIRE |
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