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Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important rolenot only in the development of mathematical and logical skills, but also in theemotional and social development. In this paper, we address the problem ofgenerating targeted starting positions for such games. This can facilitate newapproaches for bringing novice players to mastery, and also leads to discoveryof interesting game variants. We present an approach that generates startingstates 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 forplayer~$1$ to win, and the expertise levels of the two players. Our approachleverages symbolic methods and iterative simulation to efficiently search theextremely large state space. We present experimental results that includediscovery of states of varying hardness levels for several simple grid-basedboard 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 beenplayed as the default start state is heavily biased.

Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games