Can LLMs build multi-agent game world models without training?
PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
This paper introduces PIANIST, a framework for using LLMs as world models in multi-agent, partially observable settings. PIANIST decomposes the world model into seven components (Partitioned functions, Information sets, Action functions, Number of actors, Information realization functions, State spaces, and Transition-reward functions), which are generated by prompting an LLM with the game rules and a Python template. This allows for efficient planning using methods like Monte Carlo Tree Search (MCTS). Key to LLM-based multi-agent systems is PIANIST's ability to handle partial observability and complex action spaces (especially in language-based games) by using the LLM to suggest plausible actions, improving search efficiency and mitigating LLM bias. The framework shows promising zero-shot performance in games like GOPS and Taboo, demonstrating the potential of LLMs for generating effective world models in multi-agent scenarios.