Towards Urban Planing AI Agent in the Age of Agentic AI

TL;DR

This paper explores the integration of agentic AI into urban planning, emphasizing the need for adaptable, domain-informed AI systems that support participatory urban design processes.

Contribution

It identifies key limitations in current generative urban planning AI and proposes a future research direction towards agentic, participatory urban AI planners.

Findings

Current models rely on predefined generative structures.

Existing tools developed by urban planners are underutilized.

A new synthesis of agentic AI and participatory urbanism is proposed.

Abstract

Generative AI, large language models, and agentic AI have emerged separately of urban planning. However, the convergence between AI and urban planning presents an interesting opportunity towards AI urban planners. Existing studies conceptualizes urban planning as a generative AI task, where AI synthesizes land-use configurations under geospatial, social, and human-centric constraints and reshape automated urban design. We further identify critical gaps of existing generative urban planning studies: 1) the generative structure has to be predefined with strong assumption: all of adversarial generator-discriminator, forward and inverse diffusion structures, hierarchical zone-POI generative structure are predefined by humans; 2) ignore the power of domain expert developed tools: domain urban planners have developed various tools in the urban planning process guided by urban theory, while existing pure neural networks based generation ignore the power of the tools developed by urban planner practitioners. To address these limitations, we outline a future research direction agentic urban AI planner, calling for a new synthesis of agentic AI and participatory urbanism.