Summary of Engineering Features to Improve Pass Prediction in Soccer Simulation 2d Games, by Nader Zare et al.
Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games
by Nader Zare, Mahtab Sarvmaili, Aref Sayareh, Omid Amini, Stan Matwin Amilcar Soares
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 Soccer Simulation 2D (SS2D) uses Deep Neural Networks (DNN) and Random Forest (RF) to model passing behavior in a two-dimensional soccer game. The goal is to predict the actions of opponents and teammates, enabling better resource management and goal scoring. To achieve this, an embedded data extraction module records agent decision-making online, followed by four data sorting techniques for training data preparation. The trained models are evaluated by playing against top RoboCup 2019 teams with distinct strategies. Feature group importance is examined to improve pass prediction in SS2D games, achieving up to 10% performance gain when playing against top teams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Soccer Simulation 2D is a game where passing is key! To make the game better, researchers used special computer programs called Deep Neural Networks and Random Forest to understand how players make decisions. They created a way to collect data on these decisions and then used it to train the models. The models were tested by playing against strong teams from a big competition, and they did really well! This means that the game can now be even more realistic and fun. |
Keywords
* Artificial intelligence * Random forest
