StarCraft Winner Prediction

Ravari, Yaser Norouzzadeh; Bakkes, Sander; Spronck, Pieter

doi:10.1609/aiide.v12i2.12887

Cited by 9 publications

(4 citation statements)

References 17 publications

(19 reference statements)

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“…Related publications focused on in-game player performance analyses and psychological, technical, mechanical or physiological indices. These studies were conducted with use of various video games such as: Overwatch 35,36 , League of Legends [37][38][39][40][41] , Dota 2 [42][43][44][45][46] , StarCraft [47][48][49] , StarCraft II [50][51][52][53][54] , Heroes of the Storm 42 , Rocket League 55 , and Counter-Strike: Global Offensive [56][57][58][59][60] , among others 61 . In some cases a comparison between professional and recreational players was conducted.…”

Section: Related Workmentioning

confidence: 99%

SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

Białecki,

Jakubowska,

Dobrowolski

et al. 2023

Sci Data

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As a relatively new form of sport, esports offers unparalleled data availability. Our work aims to open esports to a broader scientific community by supplying raw and pre-processed files from StarCraft II esports tournaments. These files can be used in statistical and machine learning modeling tasks and compared to laboratory-based measurements. Additionally, we open-sourced and published all the custom tools that were developed in the process of creating our dataset. These tools include PyTorch and PyTorch Lightning API abstractions to load and model the data. Our dataset contains replays from major and premiere StarCraft II tournaments since 2016. We processed 55 “replaypacks” that contained 17930 files with game-state information. Our dataset is one of the few large publicly available sources of StarCraft II data upon its publication. Analysis of the extracted data holds promise for further Artificial Intelligence (AI), Machine Learning (ML), psychological, Human-Computer Interaction (HCI), and sports-related studies in a variety of supervised and self-supervised tasks.

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Section: Related Workmentioning

confidence: 99%

SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

Białecki,

Jakubowska,

Dobrowolski

et al. 2023

Sci Data

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show abstract

“…Within the StarCraft community, tasks associated with unit control fall under the umbrella of micro-management, while macro-management encompasses the higher-level game strategy a player pursues. An essential aspect of macro-management is global state evaluation, involving predicting the likelihood of winning based on the current state [8] [14][15] [16]. Build order prediction anticipates the next steps in terms of training, construction, or research given the current state [17] [18].…”

Section: Related Workmentioning

confidence: 99%

Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset

Zong,

Lin,

Wang

et al. 2023

JTPES

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Macro-management is a crucial aspect of real-time strategy (RTS) games like StarCraft II, which involves high-level decision-making processes such as resource allocation, unit production, and technology development. The MSC dataset, as presented in the original paper, provided an initial platform for researchers to investigate macro-management tasks using deep learning models. However, there are limitations to the dataset and existing baseline models that call for improvement. In this paper, we discuss the challenges and opportunities in enhancing macro-management research in StarCraft II. We propose improvements to the dataset by incorporating new features, addressing data imbalance, and updating preprocessing techniques. Furthermore, we review recent research and state-of-the-art methods in RTS games, such as attention mechanisms, graph neural networks, and reinforcement learning, that could be applied to improve existing tasks or introduce new research directions. We also present our experimental results, highlighting the effectiveness of the proposed improvements and novel approaches. Our goal is to inspire the research community to explore advanced AI techniques and strategies in macro-management and contribute to the development of more capable AI agents in complex RTS games.

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“…If the opponent's strategy is predicted, it helps develop AI performance through the use of counter strategy accordingly. Similarly, some studies design mathematical models to predict the winner [14,15]. These studies have shown that they can help predict the outcome of the game.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction for StarCraft II League Prediction

Lee

Ahn

2021

Electronics

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In a player-versus-player game such as StarCraft II, it is important to match players with others with similar skills. Studies modeling player skills were conducted, with 47.3% and 61.3% performance. In order to improve the performance, we collected 46,398 replays and compared features extracted from six sections of replays. Through the comparison of the six datasets we created, we propose a method for extracting features from a single replay. Two algorithms, k-Nearest Neighbors and Random Forest, which are most commonly used in related studies, are compared. Our research showed a outperforming accuracy of 75.3% compared to previous works. Although no direct comparison has been made with the current system, we conclude that our research can replace the placement games of five rounds.

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StarCraft Winner Prediction

Cited by 9 publications

References 17 publications

SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset

Feature Extraction for StarCraft II League Prediction

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