Computer algorithms systematically defeat the world's best chess players. The algorithms' success comes down to their ability to predict the outcome of a game based on exponentially more possibilities than a human could process, so they can almost always make the optimal decision. Computerized players in games such as chess and Connect 4 are available for anyone to play on the internet.

Today, the esports industry is exploding, and competition is intense for games such as Counterstrike: Global Offensive (CSGO) and Fortnite. The same information about game state which is used by computerized algorithms for chess and Connect 4 is stored in centralized databases for esports. Often, these databases are only accessible to game developers, but some datasets have been released for professional games.

High school student Allen Rubin conducted a research project on predicting round and game winners in CSGO. Alongside a research mentor in the Jinso research advising program, Allen developed machine learning models to make predictions based on the current state of the game.

CSGO is a turn-based game which involves two teams competing to win 16 rounds first. Hence, there are winners for each round and overall games. After each round, players earn in-game money which they can spend on equipment. Allen's machine learning models accounted for several variables regarding the game state, including each player's equipment and in-game balances.

Allen could assume that players attempt to win games, but he recognized that they are not always trying to win rounds. "Tanking" is a common strategy where players intentionally lose a round to earn a larger "loss bonus", which allows them to buy better equipment for the next round. Hence, a player who appears to be in a better position to win a round might intentionally lose, confusing machine learning algorithms. With a large enough dataset, an algorithm may be able to learn the game states in which a player is more likely to tank. However, not enough data has been released by the game developers for such an analysis.

For many video games, there are no datasets regarding game state which are public. Allen focused on CSGO because a set of round-based data for 45 professional matches was available online. He created a training and testing dataset, using standard machine learning principles to train his algorithms. Despite the small sample size, Allen was able to develop an algorithm which predicted significantly more accurate than the null hypothesis of a 50 percent success rate. He tested several machine learning approaches and found the Random Forest Classifier to achieve the best results.

Allen points out that if developers made more data about game states available, more accurate models could be developed for a wider variety of games. Computerized players could be generated for e sports competition which are calibrated to different skill levels. Betting markets for e sports could also be enhanced, which display real-time probabilities of game outcomes during competition. In turn-based games, advice on the best strategies in any given situation could be provided through machine learning.

Allen shared his research online which is freely available to read and download. He will graduate from Stevenson High School in Lincolnshire, Illinois in May 2022 and plans to begin college in the fall.

GitHub is a popular platform used by computer scientists to manage their collaborative projects, but a similar program does not exist for academic work. There is no standard platform to create work, connect with others, and share work in one place. Most platforms only fall into one or two of these categories.The Jinso collaboration tool is a better way for groups to work on projects. By bringing the entire academic collaboration process onto one tool, it simplifies workflows and communication.The first steps for using the Jinso platform are:

Create an account
Create your first group

Once a user builds a network, they can create new Groups that consist of their network members. By default, the creator of a group is the admin. The most common Group is a research group, but the platform can manage several other types of academic projects. Platform users can create study groups for sharing course materials or groups of club members for extracurricular work.The admin of the Group has the ability to add new members at any time.
Admins are also responsible for creating Projects within Groups.

A Project for a research group is usually a research paper, but Projects can also be other forms of documents that could benefit from discussion and revisions. Examples include study guides, business plans, articles, and essays. Each Group can have an unlimited number of Projects within it, and all Projects within a Group are shared among the same members. 

Once a user builds a network, they can create new Groups that consist of their network members. By default, the creator of a group is the admin. The most common Group is a research group, but the platform can manage several other types of academic projects.

Platform users can create study groups for sharing course materials or groups of club members for extracurricular work.The admin of the Group has the ability to add new members at any time. Admins are also responsible for creating Projects within Groups.

A Project for a research group is usually a research paper, but Projects can also be other forms of documents that could benefit from discussion and revisions. Examples include study guides, business plans, articles, and essays. Each Group can have an unlimited number of Projects within it, and all Projects within a Group are shared among the same members. 

Example of Research group
Revisions of the paper

When a new Project is created, an initial revision must be shared. This can either be plain text or a PDF.
The Project will be immediately visible to all Group members with the first revision shown. Group members can comment on the revision with questions or feedback, and others can reply to comments.When another revision of the paper has been completed, the Group admin can add a new revision to the same Project.
The revision will become visible above the prior revision, and it will have a new comment box associated with it. Projects make it simple to keep track of a paper’s entire revision history and discussions at each stage. 

For each revision, Group admins can also create subtasks. Arrows allow Group members to view all of the different subtasks and comment on them individually. Subtasks allow a paper to be analyzed in unique components. For example, a research paper can have a unique subtask for each of its sections, and collaborators can discuss them all separately in the comment boxes. Jinso is a quicker way to collaborate on long-term projects. It makes it easier to connect, share, and manage the development of ideas and papers. You can create a Jinso account and start using the platform today for your research and academic needs at jinso.io.

Computer algorithms systematically defeat the world's best chess players. The algorithms' success comes down to their ability to predict the outcome of a game based on exponentially more possibilities than a human could process, so they can almost always make the optimal decision. Computerized players in games such as chess and Connect 4 are available for anyone to play on the internet.

Today, the esports industry is exploding, and competition is intense for games such as Counterstrike: Global Offensive (CSGO) and Fortnite. The same information about game state which is used by computerized algorithms for chess and Connect 4 is stored in centralized databases for esports. Often, these databases are only accessible to game developers, but some datasets have been released for professional games.

High school student Allen Rubin conducted a research project on predicting round and game winners in CSGO. Alongside a research mentor in the Jinso research advising program, Allen developed machine learning models to make predictions based on the current state of the game.

CSGO is a turn-based game which involves two teams competing to win 16 rounds first. Hence, there are winners for each round and overall games. After each round, players earn in-game money which they can spend on equipment. Allen's machine learning models accounted for several variables regarding the game state, including each player's equipment and in-game balances.

Allen could assume that players attempt to win games, but he recognized that they are not always trying to win rounds. "Tanking" is a common strategy where players intentionally lose a round to earn a larger "loss bonus", which allows them to buy better equipment for the next round. Hence, a player who appears to be in a better position to win a round might intentionally lose, confusing machine learning algorithms. With a large enough dataset, an algorithm may be able to learn the game states in which a player is more likely to tank. However, not enough data has been released by the game developers for such an analysis.

For many video games, there are no datasets regarding game state which are public. Allen focused on CSGO because a set of round-based data for 45 professional matches was available online. He created a training and testing dataset, using standard machine learning principles to train his algorithms. Despite the small sample size, Allen was able to develop an algorithm which predicted significantly more accurate than the null hypothesis of a 50 percent success rate. He tested several machine learning approaches and found the Random Forest Classifier to achieve the best results.

Allen points out that if developers made more data about game states available, more accurate models could be developed for a wider variety of games. Computerized players could be generated for e sports competition which are calibrated to different skill levels. Betting markets for e sports could also be enhanced, which display real-time probabilities of game outcomes during competition. In turn-based games, advice on the best strategies in any given situation could be provided through machine learning.

Allen shared his research online which is freely available to read and download. He will graduate from Stevenson High School in Lincolnshire, Illinois in May 2022 and plans to begin college in the fall.

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