Summary of Apt: Architectural Planning and Text-to-blueprint Construction Using Large Language Models For Open-world Agents, by Jun Yu Chen et al.
APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents
by Jun Yu Chen, Tao Gao
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper presents APT, a Large Language Model (LLM)-driven framework for autonomous agents to construct complex structures in Minecraft. Unlike previous approaches, APT leverages LLMs’ spatial reasoning capabilities to generate architectural layouts and blueprints that the agent can execute under zero-shot or few-shot learning scenarios. The agent incorporates memory and reflection modules for lifelong learning, adaptive refinement, and error correction. To evaluate performance, the paper introduces a comprehensive benchmark testing creativity, spatial reasoning, adherence to in-game rules, and multimodal instruction integration. Experimental results using GPT-based LLM backends demonstrate the agent’s capacity to interpret extensive instructions, produce complex structures with internal functionalities, and reuse accumulated experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about teaching computers how to build things in a video game called Minecraft. They created a new way for computers to learn by themselves, which helps them make better decisions and correct mistakes. The computer can even remember what it learned before, so it gets smarter over time! To test this, they made a special test with lots of different tasks that the computer had to do, like building certain things or following instructions. They found out that this new way works really well and helps the computer make better choices. |
Keywords
» Artificial intelligence » Few shot » Gpt » Large language model » Zero shot
