![]() ![]() ![]() So what people and computers do is use “ heuristics” (gut guesses) to assess the “value” of different positions – estimating which player they think will win. And they certainly could never prove it – a proof would generally require too many calculations, examining every leaf of an exponential game tree. But realistically, although computers are much better at calculating and picking future moves than humans, for many positions not even they can tell for sure whether a position is winning, losing or drawing. ![]() This matches ’s statement that it evaluates whether moves “surpass … confirmed clean play” from the greats.īut how do you measure which moves are better than others? In theory, a chess position is either “winning” (you can guarantee a win), “losing” (the other player can) or “drawing” (neither can), and a good move would be any move that doesn’t make your position worse. Presumably, similar models are used to detect cheating.Ī recent study suggested that, in addition to predicting how likely a human would be to make a certain move, it’s also important to account for how good that move is. In fact, it has different models of individual famous chess players, and you can actually play against them. For example, researchers have investigated how lots of moves from a player can be analysed collectively to detect anomalies.Ĭ openly uses machine learning to predict which moves might be made by a human in any given position. To become very confident that someone cheats at a game, you have to look at lots of moves. So according to that machine learning model of human Go players, if you saw a person play Move 37, it would be evidence that they didn’t come up with the idea themselves. As lead researcher David Silver noted in the documentary AlphaGo, “AlphaGo said there was a 1/10,000 probability that Move 37 would have been played by a human player.” One of the AI’s famous moves in the game was “Move 37”. So DeepMind taught its AI to estimate the probability that a human would make any given move from any given position.ĪlphaGo famously beat human rival Lee Sedol in 2017. Given lots of examples of positions from human games (the dataset) and an example of a human move from each such position (the label), machine learning algorithms can be trained to predict labels at new data points. ![]() Predicting human moves is a supervised learning problem, the bread and butter of machine learning. When AI company DeepMind developed the program AlphaGo, which could play the strategy game Go, it was taught to predict which moves a human would make from any given position. Luckily, research can shed light on which approach the website may be using. Though legal and practical considerations prevent from revealing the full set of data, metrics and tracking used to evaluate games in our fair-play tool, we can say that at the core of ’s system is a statistical model that evaluates the probability of a human player matching an engine’s top choices, and surpassing the confirmed clean play of some of the greatest chess players in history. While Nieman has admitted to sometimes having cheated in previous online games, he has strongly denied ever cheating at a live chess tournament.īut how does, the world’s biggest chess website, decide that a player has probably cheated? It can’t show the world the code it uses, or else would-be cheaters would know exactly how to avoid detection. Another participant, the Russian Grandmaster Ian Nepomniachtchi, called Niemann’s performance “more than impressive”. ![]()
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