RuleSmith: Multi-Agent LLMs for Automated Game Balancing
#RuleSmith #LLM #Game Balancing #Multi-agent systems #Bayesian optimization #CivM #Machine Learning
📌 Key Takeaways
- RuleSmith is the first framework to use multi-agent LLMs for the autonomous balancing of game rules.
- The system combines LLM self-play with Bayesian optimization to explore and refine complex rule spaces.
- The technology was successfully tested on CivM to prove its ability to handle strategic game balancing.
- This framework significantly reduces the need for manual playtesting and human expert intuition in game design.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Game Development, Automation
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Bayesian optimization
Statistical optimization technique
Bayesian optimization is a sequential design strategy for global optimization of black-box functions, that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions. With the rise of artificial intelligence innovation in the 21st century, Bayesian optim...
🔗 Entity Intersection Graph
Connections for Large language model:
- 🌐 Reinforcement learning (7 shared articles)
- 🌐 Machine learning (5 shared articles)
- 🌐 Theory of mind (2 shared articles)
- 🌐 Generative artificial intelligence (2 shared articles)
- 🌐 Automation (2 shared articles)
- 🌐 Rag (2 shared articles)
- 🌐 Scientific method (2 shared articles)
- 🌐 Mafia (disambiguation) (1 shared articles)
- 🌐 Robustness (1 shared articles)
- 🌐 Capture the flag (1 shared articles)
- 👤 Clinical Practice (1 shared articles)
- 🌐 Wearable computer (1 shared articles)
📄 Original Source Content
arXiv:2602.06232v1 Announce Type: cross Abstract: Game balancing is a longstanding challenge requiring repeated playtesting, expert intuition, and extensive manual tuning. We introduce RuleSmith, the first framework that achieves automated game balancing by leveraging the reasoning capabilities of multi-agent LLMs. It couples a game engine, multi-agent LLMs self-play, and Bayesian optimization operating over a multi-dimensional rule space. As a proof of concept, we instantiate RuleSmith on CivM