Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development. In this paper, we address the problem of generating targeted starting positions for such games. This can facilitate new approaches for bringing novice players to mastery, and also leads to discovery of interesting game variants. We present an approach that generates starting states of varying hardness levels for player~$1$ in a two-player board game, given rules of the board game, the desired number of steps required for player~$1$ to win, and the expertise levels of the two players. Our approach leverages symbolic methods and iterative simulation to efficiently search the extremely large state space. We present experimental results that include discovery of states of varying hardness levels for several simple grid-based board games. The presence of such states for standard game variants like $4 \times 4$ Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.

本研究探讨了生成棋盘游戏的有针对性的起始位置的问题，利用符号方法和迭代模拟搜索了巨大的状态空间，发现了多种不同难度级别的状态，可以方便新手玩家的学习和掌握，同时也会带来有趣的游戏变体。

简单传统棋类游戏替代起始位置的自动生成