Automatic Design of Balanced Board Games

TLDR

AI techniques are widely used for computer opponents, yet designing balanced games is more challenging, leading to dynamic balancing methods that adjust engine parameters during play. This paper attempts to use AI to design balanced board games by modifying the rules themselves rather than just tuning parameters. The authors employ a commercial general game‑playing engine and a genetic algorithm that searches the space of turn‑based board game rules for designs that are equally hard to win from either side and rarely draw. The genetic algorithm outperforms a random‑search strategy with equivalent computational effort.

Abstract

AI techniques are already widely used in game software to provide computer-controlled opponents for human players. However, game design is a more-challenging problem than game play. Designers typically expend great effort to ensure that their games are balanced and challenging. Dynamic game-balancing techniques have been developed to modify a game-engine’s parameters in response to user play. In this paper we describe a first attempt at using AI techniques to design balanced board games like checkers and Go by modifying the rules of the game, not just the rule parameters. Our approach involves the use of a commercial general game-playing (GGP) engine that plays according to rules that are specified in a general game-definition language. We use a genetic algorithm (GA) to search the space of game rules, looking for turn-based board games that are well balanced, i.e., those that the GGP engine in self-play finds equally hard to win from either side and rarely draws. The GA finds better games than a random-search strategy that uses equivalent computational effort.