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Tuesday, January 25, 2022

Can AlphaZero Solving Problems and Rule Variations

 Sharing variations of a problem.   All problems have variations, which specify their context, can this give us a hint for other solutions?

Reimagining Chess with AlphaZero

By Nenad Tomašev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik

Communications of the ACM, February 2022, Vol. 65 No. 2, Pages 60-66 10.1145/3460349

Modern chess is the culmination of centuries of experience, as well as an evolutionary sequence of rule adjustments from its inception in the 6th century to the modern rules we know today.17 While classical chess still captivates the minds of millions of players worldwide, the game is anything but static. Many variants have been proposed and played over the years by enthusiasts and theorists.8,20 They continue the evolutionary cycle by altering the board, piece placement, or the rules—offering players "something subtle, sparkling, or amusing which cannot be done in ordinary chess."1

Key Insights

Technological progress is the new driver of the evolutionary cycle. Chess engines increase in strength, and players have access to millions of computer games and volumes of opening theory. Consequently, the number of decisive games in super-tournaments has declined, and it takes longer for players to move from home preparation to playing original moves on the board.14 While classical chess remains a fascinating game and is unlikely to ever fall out of fashion, alternative variants provide an avenue for more creative play. In Fischer random chess, the brainchild of former world champion Bobby Fischer, the initial position is randomized to counter the dominance of opening preparation in a game.7 One could consider not only entirely new ideas, but also reassess some of the newer additions to the game. For example, the "castling" move was only introduced in its current form in the 17th century. What would chess have been like had castling not been incorporated into the rules? Without recourse to repeating history, we reimagine chess and address such questions in silico with AlphaZero.25

AlphaZero is a system that can learn superhuman chess strategies from scratch without any human supervision.19,22 It represents a milestone in artificial intelligence (AI), a field that has ventured down the corridors of chess more than once in search of challenges and inspiration. Throughout the history of computer chess, the focus was on creating systems that could spar with top human players over the board.3 Computer chess has progressed steadily since the 1950s, with better-tuned evaluation functions and enhanced search algorithms deployed on increasingly more computational resources.2,3,9,13,18,24 Alan Turing already envisioned more in 1953 by asking, "Could one make a machine to play chess, and to improve its play, game by game, profiting from its experience?"27 Unlike its predecessors, AlphaZero learns its policy from scratch from repeated self-play games, answering the second part of Turing's question. The result is a unique approach to playing classical chess22 and a new era in the development of chess engines, as spear-headed by Leela Chess Zero.15

AlphaZero's ability to continually improve its understanding of the game, and reach superhuman playing strength in classical chess and Go,25 lends itself to the question of assessing chess variants and potential variants of other board games in the future. Provided only with the implementation of the rules, it is possible to effectively simulate decades of human experience in a day, opening a window into top-level play of each variant. In doing so, computer chess completes the circle, from the early days of pitting man vs. machine to a collaborative present of man with machine, where AI can empower players to explore what chess is and what it could become.11

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