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Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess

arXiv.org Artificial Intelligence

It is non-trivial to design engaging and balanced sets of game rules. Modern chess has evolved over centuries, but without a similar recourse to history, the consequences of rule changes to game dynamics are difficult to predict. AlphaZero provides an alternative in silico means of game balance assessment. It is a system that can learn near-optimal strategies for any rule set from scratch, without any human supervision, by continually learning from its own experience. In this study we use AlphaZero to creatively explore and design new chess variants. There is growing interest in chess variants like Fischer Random Chess, because of classical chess's voluminous opening theory, the high percentage of draws in professional play, and the non-negligible number of games that end while both players are still in their home preparation. We compare nine other variants that involve atomic changes to the rules of chess. The changes allow for novel strategic and tactical patterns to emerge, while keeping the games close to the original. By learning near-optimal strategies for each variant with AlphaZero, we determine what games between strong human players might look like if these variants were adopted. Qualitatively, several variants are very dynamic. An analytic comparison show that pieces are valued differently between variants, and that some variants are more decisive than classical chess. Our findings demonstrate the rich possibilities that lie beyond the rules of modern chess.


AI ruined chess. Now it's making the game beautiful again

#artificialintelligence

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.


DeepMind's AI is helping to re-write the rules of chess

#artificialintelligence

In the game between chess and artificial intelligence, Google DeepMind's researchers have made yet another move, this time teaming up with ex-chess world champion Vladimir Kramnik to design and trial new AI-infused variants of the game. With the objective of improving the design of balanced sets of game rules, the research team set out to discover the best tweaks they could possibly give to the centuries-old board game, in an ambitious effort to refresh chess dynamics thanks to AI. The scientists used AlphaZero, an adaptive learning system that can teach itself new rules from scratch and achieve superhuman levels of play, to test the outcomes of nine different chess variants that they pre-defined with Kramnik's help. For each variant, AlphaZero played tens of thousands of games against itself, analyzing every possible move for any given chessboard condition, and generating new strategies and gameplay patterns. Kramnik and the researchers then assessed what games between human players might look like if these variants were adopted, to find out whether different sets of rules might improve the game.


DeepMind's AI is helping to re-write the rules of the chess

ZDNet

In the game between chess and artificial intelligence, Google DeepMind's researchers have made yet another move, this time teaming up with ex-chess world champion Vladimir Kramnik to design and trial new AI-infused variants of the game. With the objective of improving the design of balanced sets of game rules, the research team set out to discover the best tweaks they could possibly give to the centuries-old board game, in an ambitious effort to refresh chess dynamics thanks to AI. The scientists used AlphaZero, an adaptive learning system that can teach itself new rules from scratch and achieve superhuman levels of play, to test the outcomes of nine different chess variants that they pre-defined with Kramnik's help. For each variant, AlphaZero played tens of thousands of games against itself, analyzing every possible move for any given chessboard condition, and generating new strategies and game-play patterns. Kramnik and the researchers then assessed what games between human players might look like if these variants were adopted, to find out whether different sets of rules might improve the game.


Retired Chess Grandmaster, AlphaZero AI Reinvent Chess

#artificialintelligence

Russian chess grandmaster Vladimir Kramnik is working with DeepMind's chess program, AlphaZero, to analyze new variants in an attempt to reinvent the popular strategy board game. Vladimir Kramnik, Classical World Chess Champion from 2000 to 2006, has proposed nine new chess variants last Wednesday, September 9. He has worked together with the artificial intelligence (AI) laboratory DeepMind, a subsidiary of Google's parent company Alphabet Inc, to evaluate his proposals with the help of the AlphaZero AI. In a report submitted to Cornell University, Nenad Tomašev, Ulrich Paquet, and Demis Hassabis from DeepMind worked with grandmaster Vladimir Kramnik to assess game balance in the new variations with help from AlphaZero. It defines AlphaZero as "a reinforcement learning system that can learn near-optimal strategies for any rules set from scratch without any human supervision, and provides an in silico alternative for game balance assessment."