Summary of Predicting Outcomes in Video Games with Long Short Term Memory Networks, by Kittimate Chulajata et al.
Predicting Outcomes in Video Games with Long Short Term Memory Networks
by Kittimate Chulajata, Sean Wu, Fabien Scalzo, Eun Sang Cha
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LSTMs-based approach aims to enhance audience engagement in video game tournaments by predicting win-lose outcomes in real-time. By leveraging the health indicator of each player as a time series, the method demonstrates efficient predictions using Super Street Fighter II Turbo as a proof-of-concept. Compared to state-of-the-art Transformer models found in large language models (LLMs), the LSTMs-based approach showcases competitive performance. This research has implications for predictive analysis in arcade games and opens up avenues for future exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a way to predict who will win or lose in video game tournaments using real-time data. They used a type of AI called an LSTM (Long Short Term Memory Network) that looks at how well each player is doing over time. The team tested their approach using the classic arcade game Super Street Fighter II Turbo and compared it to other methods, like those found in big language models. Their goal was to make predictions quickly and accurately so that audiences could be more engaged in major tournament events. |
Keywords
* Artificial intelligence * Lstm * Time series * Transformer
