We propose Expected Work Search (EWS), a new game solving algorithm. EWS combines win rate estimation, as used in Monte Carlo Tree Search, with proof size estimation, as used in Proof Number Search. The search efficiency of EWS stems from minimizing a novel notion of Expected Work, which predicts the expected computation required to solve a position. EWS outperforms traditional solving algorithms on the games of Go and Hex. For Go, we present the first solution to the empty 5x5 board with the commonly used positional superko ruleset. For Hex, our algorithm solves the empty 8x8 board in under 4 minutes. Experiments show that EWS succeeds both with and without extensive domain-specific knowledge.

翻译：我们提出期望工作量搜索（EWS），这是一种全新的游戏求解算法。EWS 将蒙特卡洛树搜索中使用的胜率估计与证明数搜索中使用的证明规模估计相结合。EWS 的搜索效率源于最小化一种新颖的期望工作量概念，该概念预测求解某个局面所需的预期计算量。EWS 在围棋和六贯棋游戏中优于传统的求解算法。对于围棋，我们首次求解了带有常用位置超劫规则的5x5空棋盘；对于六贯棋，我们的算法在4分钟内求解了8x8空棋盘。实验表明，EWS 在具备和不具备大量领域特定知识的情况下均能成功运行。