Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games
This addresses the need for adaptive learning tools in educational and recreational contexts by facilitating novice player mastery and uncovering new game variants, though it is incremental in applying symbolic methods to a specific domain.
The paper tackles the problem of generating targeted starting positions for simple board games like Tic-Tac-Toe to create varying hardness levels, enabling new approaches for player training and discovery of game variants, with experimental results showing discovery of such states for games including 4x4 Tic-Tac-Toe.
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.