Urban planning is tricky - governments push top-down changes while locals want bottom-up ideas. It’s hard to find compromises that make everyone happier.

A new research paper proposes using Multi-Agent Reinforcement Learning (MARL) to vote on land use. Some agents represent officials, others are for residents.

The AI is trained to balance competing interests. It learns to optimize for “consensus rewards” that keep all sides content. The AI acted like an impartial mediator to find win-win solutions.

Testing on a real neighborhood showed the AI model:

• Created more sustainable land use per city goals
• Improved the variety of housing/shops to liven up the area
• Made the end results more fair for lower/middle/upper income folks

There’s more details on how the model was evaluated in the paper. There were a number of different metrics used to score the model’s results.

I like how they turned urban planning into a spatial graph that the AI can process. This seems like a pretty interesting approach - although there are some limits like relying on a lot of land parcel data that seems hard to find for larger communities.

TLDR: AI helps find compromises in urban planning that balance government and community interests more fairly.

Full summary is here. Paper is here.