Summary of Large Language Model For Participatory Urban Planning, by Zhilun Zhou et al.
Large Language Model for Participatory Urban Planning
by Zhilun Zhou, Yuming Lin, Depeng Jin, Yong Li
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper introduces an innovative framework for participatory urban planning using Large Language Models (LLMs). The authors propose a multi-agent collaboration approach that simulates the participatory process by emulating planners and thousands of residents with diverse profiles. The framework generates land-use plans for urban regions, considering the needs of residents. Specifically, it involves an initial plan from the planner, followed by discussions among residents who provide feedback based on their profiles. The authors also adopt a fishbowl discussion mechanism to improve efficiency. Experiments show that this approach outperforms human experts in terms of service accessibility and ecology metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to help people work together to plan cities. It’s like a big game where computers act like city planners and thousands of people with different ideas and needs. The goal is to create plans that make everyone happy. The authors tested this method in two real cities and found it works really well. |
