In this paper, we improve AlphaStar's league training

Discovering features that set elite players apart is of great significance for eSports coaches as it enables them to arrange a more effective training program focused on improving those features. Moreover, finding such features results in a better evaluation of eSports players skills, which, besides coaches, is of interest for game developers to design games automatically adaptable to the players expertise. Sensor data combined with machine learning have already proved effective in classifying eSports players. However, the existing methods do not provide sufficient information about features that distinguish high-skilled players. In this paper, we propose an efficient method to find these features and then use them to classify players' skill levels. We first apply a time window to extract the players' sensor data, including heart rate, hand activities, etc., before and after game events in the League of Legends game. We use the extracted segments and symbolic transfer entropy to calculate connectivity features between sensors. The most relevant features are then selected using the newly developed consensus nested cross validation method. These features, representing the harmony between body parts, are finally used to find the optimum window size and classify players' skills. The classification results demonstrate a significant improvement by achieving 90.1% accuracy. Also, connectivity features between players gaze positions and keyboard, mouse, and hand activities were the most distinguishing features in classifying players' skills. The proposed method in this paper can be similarly applied to sportspeople data and potentially revolutionize the training programs in both eSports and sports industries

AIs can beat the world's best players at the board game Go but humans are starting to improve too. An analysis of millions of Go moves has found that professional players have been making better and more original game choices since Go-playing AIs overtook humans. Before 2016, AIs couldn't beat the world's best Go players. But this changed with an AI called AlphaGo developed by London-based research firm DeepMind. AlphaGo defeated multiple Go champions, including the then number one ranked human player.

The game of Go has been highly under-researched due to the lack of game records and analysis tools. In recent years, the increasing number of professional competitions and the advent of AlphaZero-based algorithms provide an excellent opportunity for analyzing human Go games on a large scale. In this paper, we present the ProfessionAl Go annotation datasEt (PAGE), containing 98,525 games played by 2,007 professional players and spans over 70 years. The dataset includes rich AI analysis results for each move. Moreover, PAGE provides detailed metadata for every player and game after manual cleaning and labeling. Beyond the preliminary analysis of the dataset, we provide sample tasks that benefit from our dataset to demonstrate the potential application of PAGE in multiple research directions. To the best of our knowledge, PAGE is the first dataset with extensive annotation in the game of Go. This work is an extended version of [1] where we perform a more detailed description, analysis, and application.

Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of prior work involving eSports athletes' psychophysiological data. In this paper, we present a dataset collected from professional and amateur teams in 22 matches in League of Legends video game. Recorded data include the players' physiological activity, e.g. movements, pulse, saccades, obtained from various sensors, self-reported after-match survey, and in-game data. An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level. Upon the collection of dataset we carried out its validation. In particular, we demonstrate that stress and concentration levels for professional players are less correlated, meaning more independent playstyle. Also, we show that the absence of team communication does not affect the professional players as much as amateur ones. To investigate other possible use cases of the dataset, we have trained classical machine learning algorithms for skill prediction and player re-identification using 3-minute sessions of sensor data. Best models achieved 0.856 and 0.521 (0.10 for a chance level) accuracy scores on a validation set for skill prediction and player re-id problems, respectively. The dataset is available at https://github.com/asmerdov/eSports_Sensors_Dataset.

Deep learning technology is making great progress in solving the challenging problems of artificial intelligence, hence machine learning based on artificial neural networks is in the spotlight again. In some areas, artificial intelligence based on deep learning is beyond human capabilities. It seemed extremely difficult for a machine to beat a human in a Go game, but AlphaGo has shown to beat a professional player in the game. By looking at the statistical distribution of the distance in which the Go stones are laid in succession, we find a clear trace that Alphago has surpassed human abilities. The AlphaGo than professional players and professional players than ordinary players shows the laying of stones in the distance becomes more frequent. In addition, AlphaGo shows a much more pronounced difference than that of ordinary players and professional players.

DeepMind, an offshoot of Google's parent company, debuted a computer program in January capable of beating professional players at one of the world's toughest video games. StarCraft is a military science fiction franchise set in a universe rife with conflict, where armies of opponents face off to become the most powerful. And DeepMind's program, called AlphaStar, reached StarCraft II's highest rank -- Grandmaster. It can defeat 99.8 percent of human players, according to a study published in the journal Nature in October. StarCraft is one of the most popular, difficult electronic sports in the world.

The waves of the third artificial intelligence (AI) boom are now sweeping across Japan in the same way as earlier fads did in the 1950s and the 1980s. Referring to the ongoing craze in the country, leading Japanese economic magazine Shukan toyo keizai wrote in its 5 December 2015 issue, "not a single day passes by without hearing about AI." Many companies in Japan are making AI-related announcements one after another. Seminars on AI are held in Tokyo almost every day. But the question we must ask is this: Is the development of AI good news for mankind? From early on, many people in the world outside Japan forecast a dystopian future if AI were to surpass human intelligence. To cite an early example, Bill Joy, a U.S. computer scientist dubbed the Thomas Edison of the Internet, cautioned that robots with higher intelligence may compete with humans and threaten the latter's survival when they become able to self-replicate in "Why the Future Doesn't Need Us," an article he published in 2000. More recently, British theoretical physicist and cosmologist Stephen Hawking expressed the fear that "the development of full artificial intelligence could spell the end of the human race." Speaking in concert, Microsoft founder Bill Gates also said, "I am in the camp that is concerned about the threat of super intelligence [to human beings］." Behind their concern, there is the feeling of unease that humans will stop being the owners of the highest intelligence on earth. High intelligence is the very thing that has allowed humans to consider themselves as special beings distinguished from other animals. What will happen if and when AI surpasses human intelligence? Will humans really be able to continue their dominance as rulers of the earth in this situation? Won't machines deprive humans of many intellectual jobs and dominate them, in effect? These arguments about the possible threats posed by AI have been small in number in Japan until recently, however.

In January, artificial intelligence(AI) powerhouse DeepMind announced it had achieved a major milestone in its journey towards building AI systems that resemble human cognition. AlphaStar was a DeepMind agent designed using reinforcement learning that was able to beat two professional players at a game of StarCraft II, one of the most complex real-time strategy games of all time. During the last few months, DeepMind continued evolving AlphaStar to the point that the AI agent is now able to play a full game of StarCraft II at a Grandmaster level outranking 99.8% of human players. The results were recently published in Nature and they show some of the most advanced self-learning techniques used in modern AI systems. DeepMind's milestone is better explained by illustrating the trajectory from the first version of AlphaStar to the current one as well as some of the key challenges of StarCraft II.

During one experiment, the poker bot Pluribus played against five professional players. During one experiment, the poker bot Pluribus played against five professional players. In artificial intelligence, it's a milestone when a computer program can beat top players at a game like chess. But a game like poker, specifically six-player Texas Hold'em, has been too tough for a machine to master -- until now. Researchers say they have designed a bot called Pluribus capable of taking on poker professionals in the most popular form of poker and winning.