There are also evidences suggesting that many successful ad-hoc add-ons to depth-based searches are generalized by switching to a probability-based search. Preliminary comparisons between a basic implementation of probability-based search and a basic implementation of depth-based search showed that our new probability-based approach performs moderately better than the established approach. Depth-based searches form the backbone of virtually all chess engines in existence today, and is an algorithm that has become well-established over the past half century. We also investigated the possibility of using probability thresholds instead of depth to shape search trees. Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess. The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines - all of which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters. Unlike previous attempts using machine learning only to perform parameter-tuning on hand-crafted evaluation functions, Giraffe’s learning system also performs automatic feature extraction and pattern recognition. This guy is a kind of human AlphaZero, and I have a huge respect to his dedication to improve his game against the engines.It is sad to see how people criticise him, often without even having a look on his books.This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Than is one of the reasons top engines still have considerable difficulties with them". Tsvetkov believes that close games are the future of chess: "they exhibit the deepest lines and most refined positional characteristics. In any case, if you are interested in interesting but unconventional chess books, get a copy of them (but be sure that you read first a sample in Kindle to see if you will accept his style). Although a lot of weak players are killing the author in -forum, there is a surprising positive review by the GM Smerdon (hey, also a Scandi-specialist, but with the Portuguese Gambit and not. Often he correct engines' evaluations (for example, sometimes engines see advantage in for example draw positions). He often gives a diagram, not telling which side is to play, and show his evaluation. But the book is rally useful if to learn strategy. It is rather unusual, you have to get used with his style and not very good English (but better than my) and evaluations based on centypawns. The guy really understand the weakness of these engines and know how to explore small (sometimes not that small at all) inaccuracies and convert them to great positional advantages. The games are highly interesting from a strategic and tactic point of view. Tsvetkov usually play rather quite openings, tried to close the centre and attack the king with a pawn-storm, often after having castle on the kingside and attacking on the kingside. After going some of the games I see some common patterns. As he wrote, there are no takebacks, and time control has been stricktly applied. The time control for the game differs, but those are mostly blitz or rapid games, with the author having 2 times more on average on the clock. The games were played in the period 2013-2017 against different Stockfish versions and Komodo 10. This is a collection of his winning games against Stockfish and Komodo. I recommend the books "Human versus Machine". Although his books received some negative reviews in Amazon and -forum, I think it is worth to get them. I am curious to hear if you read one of Lyudmil Tsvetkov's books on chess.
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