Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess (Paper Explained)
199#ai #chess #alphazero
Chess is a very old game and both its rules and theory have evolved over thousands of years in the collective effort of millions of humans. Therefore, it is almost impossible to predict the effect of even minor changes to the game rules, because this collective process cannot be easily replicated. This paper proposes to use AlphaZero's ability to achieve superhuman performance in board games within one day of training to assess the effect of a series of small, but consequential rule changes. It analyzes the resulting strategies and sets the stage for broader applications of reinforcement learning to study rule-based systems.
OUTLINE:
0:00 - Intro & Overview
2:30 - Alternate Chess Rules
4:20 - Using AlphaZero to assess rule change outcomes
6:00 - How AlphaZero works
16:40 - Alternate Chess Rules continued
18:50 - Game outcome distributions
31:45 - e4 and Nf3 in classic vs no-castling chess
36:40 - Conclusions & comments
Paper: https://arxiv.org/abs/2009.04374
My Video on AI Economist: https://youtu.be/F5aaXrIMWyU
Abstract:
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.
Authors: Nenad Tomašev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik
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