  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.
