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Download the Best Chess Variants: From Chess960 to Crazyhouse



The millennia-old pastime of chess has been enjoying an explosion of interest. Whether people are longing for any engaging hobby to keep themselves sane during the long wait, or they fell prey to a popular Netflix drama about a chess prodigy, competition is heating up. While games using a physical board will have to wait a bit longer for most of us, there are excellent online chess platforms and a healthy chess community available in a few clicks or thumb taps.




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If you're a chess player, you're most likely aware that there are various apps available on mobile devices. Not only are there plenty of places to play chess with other people, but also apps that help you practice endgames and various opening tactics. There are even apps geared toward kids.


Chess.com is one of the most popular chess apps available. It's also the most popular chess website, with over 100 million players worldwide. With so many players, it's easy to get a live game with someone of your skill set instantly.


While there are other apps that feature live games, Chess.com has the best matching system, ensuring you get a fair game. Chess.com also has an established investigation team looking for cheaters, which is another great benefit.


On Chess.com, similar puzzles are only included in a paid membership plan. These exercises are great for learning to find the best move on the board. The puzzles range in difficulty, and you can constantly improve your game.


Pocket Chess is the newer chess app of the two previously mentioned. Because it hasn't been around for long, it might not be as established, but some of its features make it great for learning checkmates.


The entire purpose of the app is just chess puzzles, where you have to find the checkmate in a certain number of moves. It starts out easy but gets progressively harder. As you continue, you will learn unique ways to find checkmate with common pieces. It starts out with one move checkmates and goes up to four and higher.


There are many ways to play chess online and improve your skill, but if you want a different type of game experience, Chess Royale is an excellent option. Whether you are new to chess or a seasoned veteran, Chess Royale has plenty of features that will make it worthwhile.


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While many apps like Chess.com and Lichess offer live games, there's usually a time limit, meaning each player gets 5 or 10 minutes, and if you run out of time, you lose. While this is how tournament games work, some casual players may prefer a laid-back, non-timed game.


If you love the game of chess and want to help your child learn, then you should consider the ChessKid app. Made by Chess.com, this app is a beginner-level chess app designed to teach children the game of chess.


This app is also ad-free and safe for children, with no chat features or any sort of personal information required. This makes ChessKid not only the best app to teach kids chess but also lets them have fun while staying safe.


With several great apps available for chess players, you can keep up with chess on the go. Most Grandmasters say that to get better, you must play and practice as much as possible. However, many of us have responsibilities that can make it hard to do that.


By downloading mobile apps, we can practice, play games, and learn while we're out and about. While plenty of options are available on the App Store and Play Store, you can skip the trial-and-error method with our list of the best ones.


SparkChess is an excellent way to get better at chess - learn the proper rules (including the elusive en-passant), practice openings, test strategies, use the board editor to recreate famous positions with FEN strings, replay famous games, import/export PGN games and databases (with comments and annotations) and let the computer help you. With 5 levels of difficulty and a behavior modeled to make human mistakes, this is a very fun game to play. Our online chess game also features an opening database created by analysing 145,000 games from international tournaments. There are 4 different board styles (a 2D diagram, two fixed 3D designs and a 3D rotatable board) to suit any style - from the playful kid to the serious tournament player.


SparkChess has won many awards and it was featured by Google, Blackberry and Adobe for its cross-platform capabilities. You can download SparkChess for PC, Mac, iPad, iPhone, Android phones and tablets.


Full-ride scholarships cover four years of college expenses, including tuition, fees, books, housing, and meals. Several U.S. universities offer full-ride college chess scholarships. While players with FIDE titles are most likely to be awarded full-ride college chess scholarships, players with ratings as low as 2300 US Chess have been awarded these scholarships. More ?


Are there some free chess game databases where it is possible and allowed to download games? Is it possible to use this information for my own purposes? For example, would be allowed to make a machine learning app that learned from the downloaded games?


It would almost certainly not be applied for fairly small, random downloads: it might however be applied for full download/dump of a chess database. An organization that plans to use it may add identifiable material that makes it possible to say where the information was taken from.


We trained 9 versions of Maia, one for each Elo milestone between 1100 and 1900. Maia 1100 was only trained on games between 1100-rated players, and so on. Each version learned from 12 million human games, and learns how chess is typically played at its specific level.


As a comparison, we looked at how depth-limited Stockfish does on the same prediction task. We ran various depth limits, ranging from only considering the current board (D01) to letting it search 15 plies ahead (D15). Depth-limited Stockfish is the most popular engine to play against for fun (e.g. the "Play with the Computer" feature on Lichess).


You can play against Maia yourself on Lichess! You can play Maia 1100, Maia 1500, and Maia 1900. Maia is an ongoing research project using chess as a case study for how to design better human-AI interactions. We hope Maia becomes a useful learning tool and is fun to play against. Our research goals include personalizing Maia to individual players, characterizing the kinds of mistakes that are made at each rating level, running Maia on your games and spotting repeated, predictable mistakes, and more.


The code for training Maia can be found on our Github Repo. Abstract As artificial intelligence becomes increasingly intelligent--in some cases, achieving superhuman performance--there is growing potential for humans to learn from and collaborate with algorithms. However, the ways in which AI systems approach problems are often different from the ways people do, and thus may be uninterpretable and hard to learn from. A crucial step in bridging this gap between human and artificial intelligence is modeling the granular actions that constitute human behavior, rather than simply matching aggregate human performance. We pursue this goal in a model system with a long history in artificial intelligence: chess. The aggregate performance of a chess player unfolds as they make decisions over the course of a game. The hundreds of millions of games played online by players at every skill level form a rich source of data in which these decisions, and their exact context, are recorded in minute detail. Applying existing chess engines to this data, including an open-source implementation of AlphaZero, we find that they do not predict human moves well. We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way. For a dual task of predicting whether a human will make a large mistake on the next move, we develop a deep neural network that significantly outperforms competitive baselines. Taken together, our results suggest that there is substantial promise in designing artificial intelligence systems with hum