Researchers find using MARL AI results in better urban planning outcomes
A new research paper proposes using Multi-Agent Reinforcement Learning (MARL) to vote on land use
Urban planning decisions have far-reaching impacts on cities and their residents. However, the process often sparks controversy as diverse stakeholders like governments, developers, and local communities have conflicting interests and priorities.
A new study proposes leveraging AI techniques to make urban planning more inclusive, equitable and responsive to changing needs. The research demonstrates how computational models can find optimal compromises between top-down and grassroots perspectives in land use planning.
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