Summary of Hypothetical Minds: Scaffolding Theory Of Mind For Multi-agent Tasks with Large Language Models, by Logan Cross et al.
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models
by Logan Cross, Violet Xiang, Agam Bhatia, Daniel LK Yamins, Nick Haber
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes an autonomous agent called Hypothetical Minds that utilizes large language models (LLMs) to address challenges in multi-agent reinforcement learning (MARL). The agent features a cognitively-inspired architecture with modular components for perception, memory, and hierarchical planning. A key innovation is the Theory of Mind module, which generates hypotheses about other agents’ strategies in natural language and evaluates them by refining predictions about their behavior. This approach leads to significant performance improvements over previous LLM-agent and RL baselines on various competitive, mixed motive, and collaborative domains in the Melting Pot benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s Hypothetical Minds agent uses big language models to help machines work together better. The agent has a special module that figures out what other agents are thinking and plans accordingly. This helps the agent do much better than previous attempts on similar tasks, like working with different teams or figuring out how others will act. |
Keywords
» Artificial intelligence » Reinforcement learning
