Summary of Mineland: Simulating Large-scale Multi-agent Interactions with Limited Multimodal Senses and Physical Needs, by Xianhao Yu et al.
MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs
by Xianhao Yu, Jiaqi Fu, Renjia Deng, Wenjuan Han
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 MineLand simulator bridges the gap between traditional multi-agent simulators and Vision-Language Models (VLMs) by introducing large-scale scalability, limited multimodal senses, and physical needs. This allows for more ecological and nuanced collective behavior simulations. The simulator supports 64 or more agents with limited visual, auditory, and environmental awareness, requiring active communication and collaboration to fulfill physical needs like food and resources. Additionally, an AI agent framework called Alex is introduced, inspired by multitasking theory, enabling agents to handle intricate coordination and scheduling. Experiments demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a virtual world where artificial intelligence (AI) agents work together to solve problems and achieve goals. This world is like a video game, but instead of playing for fun, the AI agents are trying to survive and thrive in a challenging environment. The MineLand simulator helps us understand how these AI agents can work together effectively by creating a virtual world that is as realistic as possible. In this world, the AI agents have limited senses and abilities, just like humans do. They need to communicate with each other and work together to get what they need to survive. |
