About AlphaZero

In a new paper from DeepMind, this time co-written by 14th world chess champion Vladimir Kramnik, the self-learning chess engine AlphaZero is used to explore the design of different variants of the game of chess, with different sets of rules. The paper is titled Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess and has been written by Deepmind's Nenad Tomasev, Ulrich Paquet, and Demis Hassabis, together with Kramnik. The Russian grandmaster has been working with DeepMind since last year, when we published his article about No-Castling chess. On Friday, September 18 Chess.com hosted a round-table discussion with GM Vladimir Kramnik, IM Danny Rensch, and researchers of Deepmind discussing their latest paper in which AlphaZero explores chess variants. In this new paper (here in PDF), No-Castling chess is one of nine chess variants that have been looked at. AlphaZero functioned as the tool to simulate decades of human play in a matter of hours, which made it possible to see what games between strong human players in these variants would potentially look like. Game design, in general, is complicated. Coming up with a new chess variant that actually works, is not easy either. The researchers write: "Designing engaging and balanced sets of game rules is non-trivial, due to difficulties in assessing the consequences of individual changes on game dynamics and appeal." Chess.com's Chief Chess Officer, International Master Danny Rensch, reviewed the paper in detail during the embargo period which Chess.com had privileged access to the games, and he created this quick breakdown.