Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games

TL;DR

This paper introduces a symbolic and simulation-based method to generate starting positions of varying difficulty for simple board games, aiding player skill development and game variant discovery.

Contribution

It presents a novel approach to generate targeted starting states for simple board games based on rules, desired difficulty, and player expertise levels.

Findings

Generated states of varying hardness for grid-based games.

Discovered new game states for standard variants like 4x4 Tic-Tac-Toe.

Enabled exploration of previously unplayed game variants.

Abstract

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.