Researchers have developed a gamified framework using multi-agent reinforcement learning (MARL) to address conflicting stakeholder objectives in urban resilience planning. The system models urban design as a digital board game where agents allocate resources to balance individual gains with collective development and resilience goals. In simulations, particularly on an 8x8 board and a 12x12 simulation of Tatsumi, Tokyo, the trained agents demonstrated improved resilience metrics like adaptability and flood resistance, though with trade-offs in vitality and density. A human-in-the-loop interface is also provided for participatory planning. AI
IMPACT Provides a novel AI-driven approach for complex urban planning scenarios with competing interests.
RANK_REASON The cluster contains an academic paper detailing a new methodology and simulation results. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.MA (Multiagent) →
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