RuleSmith: Multi-Agent LLMs for Automated Game Balancing

Game balancing is a longstanding challenge requiring repeated playtesting, expert intuition, and extensive manual tuning. We introduce RuleSmith, a framework for automated game balancing that couples multi-agent LLM self-play with Bayesian optimization over a parameterized rule space. As a proof of concept, we build CivMini, a simplified civilization-style game with two asymmetric factions governed by 12 tunable parameters. LLM agents read textual rulebooks and game states to generate legal actions, enabling fast evaluation of balance metrics such as win-rate disparity. To search the rule space efficiently, we use Bayesian optimization with acquisition-based adaptive sampling: candidates with high Expected Improvement receive more evaluation games, while exploratory candidates receive fewer. RuleSmith consistently finds near-balanced configurations and produces interpretable parameter adjustments. It provides a scalable alternative to manual playtesting. Code will be available upon acceptance.