Summary of Pianist: Learning Partially Observable World Models with Llms For Multi-agent Decision Making, by Jonathan Light et al.
PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
by Jonathan Light, Sixue Xing, Yuanzhe Liu, Weiqin Chen, Min Cai, Xiusi Chen, Guanzhi Wang, Wei Cheng, Yisong Yue, Ziniu Hu
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed PIANIST framework decomposes the world model into seven intuitive components, enabling zero-shot large language model (LLM) generation for complex decision-making tasks. By providing only a natural language description of the game and input observation formatting, the method generates a working world model for fast and efficient Monte Carlo tree search (MCTS) simulation. This framework is demonstrated to work well on two games that challenge planning and decision-making skills, without requiring domain-specific training data or explicitly defined world models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to create a map of the world for computers to make decisions quickly and efficiently. They break down this map into seven simple pieces, allowing them to generate a model from just a natural language description of the game. This means they can simulate decision-making processes without any extra training data or detailed descriptions. |
Keywords
» Artificial intelligence » Large language model » Zero shot
