RuleSmith uses multi-agent LLMs for automated game balancing

Researchers have developed RuleSmith, a novel framework that uses multi-agent large language models (LLMs) for automated game balancing. This system employs LLM agents to interpret game rules and states, proposing actions that are then validated by a game engine to estimate win rates. The framework integrates with surrogate-based optimization techniques, such as Gaussian-process Bayesian optimization, to efficiently explore a parameterized rule space and identify near-balanced configurations. Initial experiments with a simplified civilization-style game demonstrated RuleSmith's ability to achieve empirical win rates close to even, suggesting its potential for producing interpretable game adjustments without requiring task-specific policy training. AI

IMPACT This framework could streamline game development by automating complex balancing tasks, potentially leading to more refined and fair game experiences.

RANK_REASON The cluster describes a research paper detailing a new framework and methodology for game balancing using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• CivMini
• Expected-Improvement-based adaptive sampling
• Gaussian-process Bayesian optimization
• NTBEA
• RuleSmith
• TPE
• Ziyao Zeng

• paper
• product

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