Summary of Hierarchical Auto-organizing System For Open-ended Multi-agent Navigation, by Zhonghan Zhao et al.
Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation
by Zhonghan Zhao, Kewei Chen, Dongxu Guo, Wenhao Chai, Tian Ye, Yanting Zhang, Gaoang Wang
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 A novel framework for navigating complex environments in Minecraft is proposed, leveraging multi-agent systems and Long Short-Term Memory (LLM) networks. The HAS framework auto-organizes groups of agents to complete tasks, featuring a hierarchical system for planning and execution, an intra-communication mechanism for dynamic group adjustment, and a multi-modal information platform for perception. The framework is evaluated through a series of navigation tasks in Minecraft, including searching and exploring, aiming to develop embodied organizations that simulate human-like organizational structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Minecraft agents can work together or alone to achieve goals. But, it’s hard to get them to talk and work together effectively. This paper creates a special framework called HAS that helps groups of smart agents (LLM-based) navigate the Minecraft world. The framework has three main parts: 1) planning and execution; 2) adjusting groups as needed; and 3) using multiple senses like vision, text, and sound to understand goals. It’s tested in Minecraft with tasks like searching and exploring. |
Keywords
» Artificial intelligence » Multi modal
