PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
Jonathan Light, Sixue Xing, Yuanzhe Liu, Weiqin Chen, Min Cai, Xiusi Chen, Guanzhi Wang, Wei Cheng, Yisong Yue, Ziniu Hu·November 24, 2024
Summary
PIANIST framework decomposes complex decision-making tasks into seven components for LLMs, enabling zero-shot generation of a working world model for multi-agent, partial information scenarios. It uses game descriptions and observation formats to simulate MCTS, demonstrating effectiveness in language and non-language games without domain-specific training or explicit world model definitions. PIANIST combines state prediction, reward assignment, and information partitioning for multi-player games, using an information realization function to map information sets to hidden states. It integrates with Monte Carlo Tree Search (MCTS) by sampling hidden states and simulating actions with highest UCT values, averaging across states within the same information set. PIANIST, using an LLM-generated world model, performed comparably to ground-truth models in games, indicating accurate world model creation. It outperformed direct LLM action queries, showing enhanced planning. However, PIANIST struggled against humans in GOPS and Taboo, highlighting the need for improved LLM decision-making in complex environments. Despite this, PIANIST demonstrated strong performance, suggesting potential for more nuanced adaptations to balance decision-making across different games, enhancing robust generalization in multi-agent settings.
Introduction
Background
Overview of complex decision-making tasks in multi-agent, partial information scenarios
Importance of world models in artificial intelligence for strategic planning
Objective
To introduce PIANIST, a framework that decomposes complex tasks into seven components for LLMs
Highlighting PIANIST's capability to generate a working world model without domain-specific training or explicit definitions
Method
Data Collection
Utilization of game descriptions and observation formats for data input
Simulation of Monte Carlo Tree Search (MCTS) through data collection methods
Data Preprocessing
Techniques for preparing game descriptions and observations for PIANIST
Methods for mapping information sets to hidden states for efficient processing
PIANIST Components
State Prediction
Explanation of state prediction in multi-player games
Integration of state prediction with reward assignment and information partitioning
Reward Assignment
Mechanism for assigning rewards based on game outcomes
Role in guiding the learning process of LLMs
Information Partitioning
Division of information sets for effective decision-making
Utilization of an information realization function for mapping to hidden states
Information Realization Function
Detailed explanation of the function's role in PIANIST
How it aids in simulating actions with highest UCT values
Integration with Monte Carlo Tree Search (MCTS)
Sampling Hidden States
Process of sampling hidden states for action simulation
Action Selection
Utilization of UCT values for selecting actions within the same information set
Importance of averaging across states for enhanced planning
Performance Evaluation
Comparison with Ground-Truth Models
Results of PIANIST's performance in games compared to ground-truth models
Indicators of accurate world model creation
Outperformance of Direct LLM Action Queries
Advantages of using PIANIST over direct LLM action queries
Enhanced planning capabilities demonstrated
Limitations and Future Directions
Struggles against Humans
Analysis of PIANIST's performance against human players in specific games
Identification of areas needing improvement in LLM decision-making
Potential for Nuanced Adaptations
Discussion on enhancing PIANIST for more nuanced decision-making
Strategies for balancing decision-making across different games
Enhancing Robust Generalization
Importance of robust generalization in multi-agent settings
Suggestions for future research to improve PIANIST's performance
Conclusion
Recap of PIANIST's contributions to LLMs in complex decision-making tasks
Future prospects and implications for AI research and applications
Basic info
papers
machine learning
artificial intelligence
multiagent systems
Advanced features