Description
AI integration in Smart Cities, primarily through agent-based simulations, holds transformative potential for understanding and enhancing citizen behavior. Striking a balance between complexity and computational feasibility is essential. Our research question is, how can we make agents behave more realistically? We assumed that happiness is a motivating factor for the mobility. Insights from a survey of 130 citizens inform our weightings. We used reinforcement learning (RL) as a method and Q-learning as an algorithm to generate a baseline, further enhanced with neural networks for adaptability. This study contributes to data-driven urban design by offering efficient intelligent agent solutions. The research lays foundations for smart agents in urban design, which can be used to generate synthetic data.
Keywords
artificial intelligence, synthetic data, smart cities, deep reinforcement learning, design
| Find me @ my poster | 1,2,3,4 |
|---|
