We present new data structures for representing symmetric normal-form games. These data structures are optimized for efficiently computing the expected utility of each unilateral pure-strategy deviation from a symmetric mixed-strategy profile. The cumulative effect of numerous incremental innovations is a dramatic speedup in the computation of symmetric mixed-strategy Nash equilibria, making it practical to represent and solve games with dozens to hundreds of players. These data structures naturally extend to role-symmetric and action-graph games with similar benefits.

翻译：我们提出用于表示对称正规型博弈的新型数据结构。这些数据结构针对高效计算从对称混合策略剖面出发的每个单方面纯策略离差的期望效用进行了优化。通过众多增量创新的累积效应，对称混合策略纳什均衡的计算速度得到显著提升，使得表示和求解涉及数十至数百名参与者的博弈变得切实可行。这些数据结构可自然扩展至角色对称博弈和动作图博弈，并带来类似优势。