The StarCraft series, which has led the development of the gaming industry, has been played for more than 20 years. Moreover, in StarCraft Ⅱ, many comparisons have been made of win-loss prediction models that used in-game information in conjunction with various machine learning and deep learning algorithms.
Several of those related to win-loss predictions used machine learning and deep learning.
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Among the numerous win-loss predictive game studies, more have been conducted on the StarCraft series of games than on any other game. The machine learning and deep learning algorithms were tailored to find the relationship between various information in various games, including the StarCraft series. Recently, several studies have applied machine learning and deep learning algorithms to various games. Because MOBA games like League of Legends are not time limited, the number of game situations is almost infinite. Several battles are waged according to each champion’s and each team’s strategy, and the game results are determined through these battles. Various situations occur, depending on the gold and other items collected by each champion, points for experience, and the players’ skill levels. In it, each player chooses one champion from over the 100 champions available, and 10 champions can be played in any one game. The League of Legends poses the same difficulty. Moreover, real-time strategy games like StarCraft are not limited to game playtime, making the total number of cases in one game almost infinite. At a certain point in time, gamers can choose one from a variety of options, including harvesting resources, attacking, building buildings, and scouting. For example, in StarCraft, information concerning units, buildings, and resources exists, but how this information is combined and presented hinges on a gamer’s actions. The accurate extraction of key game situations requires an analysis of the interaction of in-game information, which is difficult because of both the sheer volume of information as well as the large number of ways it can be combined. However, accurately extracting key game situations can be challenging. By addressing this issue, viewers of game broadcasts can satisfy their curiosity about a player’s winning or losing, and gamers can improve their skills and heighten their awareness of the importance of creating and employing their own strategies as well as apprehending those of their opponents.
Extracting key game situations means using in-game information to derive important situations based on forecasting the probability of a win or a loss. In accompaniment with this growth, the problem of extracting key game situations has become an important research topic.
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Similarly, the number of people employed in e-sports, such as professional gamers, commentators, and coaching staff, continues to increase. Numerous competitions related to these games are held and broadcast. Real-time strategy games such as StarCraft, StarCraft Ⅱ, and MOBA games such as League of Legends and Dota 2 are leading the growth of the gaming industry. Various genres such as real-time strategy games, first-person shooter games (FPS), role-playing games (RPG), and multiplayer online battle arenas (MOBA) have proven especially popular. The gaming industry has grown to such an extent in popularity that one segment of it has been recognized as a new competition category, e-sports. We verified the usefulness of our methodology on a 3D-ResNet with a gradient class activation map linked to a StarCraft Ⅱ replay dataset. We then proved that the proposed method outperforms by comparing 2D-residual networks (2D-ResNet) using only one time-point information and 3D-ResNet with multiple time-point information. Second, we used gradient-weighted class activation mapping to extract information defining the key situations that significantly influenced the outcomes of the game. First, we trained a result prediction model using 3D-residual networks (3D-ResNet) and replay data to improve prediction performance by utilizing in-game spatiotemporal information. We used replay data from StarCraft Ⅱ that is similar to video data providing continuous multiple images. In this study, we propose a methodology to predict outcomes and to identify information about the turning points that determine outcomes in StarCraft Ⅱ, one of the most popular real-time strategy games. However, previous studies have mainly focused on predicting game results. The creation of winning strategies requires accurately analyzing previous games therefore, it is important to be able to identify the key situations that determined the outcomes of those games. In real-time strategy games, players collect resources, control various units, and create strategies to win.