Planning, Living and Judging: A Multi-agent LLM-based Framework for Cyclical Urban Planning

• URL: http://arxiv.org/abs/2412.20505v1
• Date: Sun, 29 Dec 2024 15:43:25 GMT
• Title: Planning, Living and Judging: A Multi-agent LLM-based Framework for Cyclical Urban Planning
• Authors: Hang Ni, Yuzhi Wang, Hao Liu,
• Abstract summary: Urban regeneration presents significant challenges within the context of urbanization. We propose Cyclical Urban Planning (CUP), a new paradigm that continuously generates, evaluates, and refines urban plans in a closed-loop. Experiments on the real-world dataset demonstrate the effectiveness of our framework as a continuous and adaptive planning process.
• Score: 5.9423583597394325
• License:
• Abstract: Urban regeneration presents significant challenges within the context of urbanization, requiring adaptive approaches to tackle evolving needs. Leveraging advancements in large language models (LLMs), we propose Cyclical Urban Planning (CUP), a new paradigm that continuously generates, evaluates, and refines urban plans in a closed-loop. Specifically, our multi-agent LLM-based framework consists of three key components: (1) Planning, where LLM agents generate and refine urban plans based on contextual data; (2) Living, where agents simulate the behaviors and interactions of residents, modeling life in the urban environment; and (3) Judging, which involves evaluating plan effectiveness and providing iterative feedback for improvement. The cyclical process enables a dynamic and responsive planning approach. Experiments on the real-world dataset demonstrate the effectiveness of our framework as a continuous and adaptive planning process.

Related papers

• Adaptive Urban Planning: A Hybrid Framework for Balanced City Development [1.8921784053120494]
  Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences. We propose a two-tier approach, with a deterministic solver optimizing basic infrastructure requirements in the city region. Four specialized planning agents, each representing distinct sub-regions, propose demographic-specific modifications to a master planner. The master planner then evaluates and integrates these suggestions to ensure cohesive urban development.
  arXiv  Detail & Related papers  (2024-12-19T19:30:42Z)
• AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation [81.32722475387364]
  Large Language Model-based agents have garnered significant attention and are becoming increasingly popular. Planning ability is a crucial component of an LLM-based agent, which generally entails achieving a desired goal from an initial state. Recent studies have demonstrated that utilizing expert-level trajectory for instruction-tuning LLMs effectively enhances their planning capabilities.
  arXiv  Detail & Related papers  (2024-08-01T17:59:46Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• PDDLEGO: Iterative Planning in Textual Environments [56.12148805913657]
  Planning in textual environments has been shown to be a long-standing challenge even for current models. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation.
  arXiv  Detail & Related papers  (2024-05-30T08:01:20Z)
• What's the Plan? Evaluating and Developing Planning-Aware Techniques for Language Models [7.216683826556268]
  Large language models (LLMs) are increasingly used for applications that require planning capabilities. We introduce SimPlan, a novel hybrid-method, and evaluate its performance in a new challenging setup.
  arXiv  Detail & Related papers  (2024-02-18T07:42:49Z)
• Large language model empowered participatory urban planning [5.402147437950729]
  This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback, solving a community-level land-use task catering to 1000 distinct interests.
  arXiv  Detail & Related papers  (2024-01-24T10:50:01Z)
• Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty [56.30846158280031]
  Task planning for embodied AI has been one of the most challenging problems. We propose a task-agnostic method named 'planning as in-painting' The proposed framework achieves promising performances in various embodied AI tasks.
  arXiv  Detail & Related papers  (2023-12-02T10:07:17Z)
• AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning [8.363841553742912]
  We introduce a Consensus-based Multi-Agent Reinforcement Learning framework for real-world land use readjustment. This framework serves participatory urban planning, allowing diverse intelligent agents as stakeholder representatives to vote for preferred land use types. By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs.
  arXiv  Detail & Related papers  (2023-10-25T17:04:11Z)
• Dual-stage Flows-based Generative Modeling for Traceable Urban Planning [33.03616838528995]
  We propose a novel generative framework based on normalizing flows, namely Dual-stage Urban Flows framework. We employ an Information Fusion Module to capture the relationship among functional zones and fuse the information of different aspects. Our framework can outperform compared to other generative models for the urban planning task.
  arXiv  Detail & Related papers  (2023-10-03T21:49:49Z)
• AdaPlanner: Adaptive Planning from Feedback with Language Models [56.367020818139665]
  Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback. To mitigate hallucination, we develop a code-style LLM prompt structure that facilitates plan generation across a variety of tasks, environments, and agent capabilities.
  arXiv  Detail & Related papers  (2023-05-26T05:52:27Z)
• Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning [57.91323079939641]
  We develop a novel human-instructed deep hierarchical generative model to generate optimal urban plans. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
  arXiv  Detail & Related papers  (2022-12-01T23:06:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.