Automated Graph Layout Optimization via Reinforcement Learning and Dynamic Constraint Satisfaction

**Abstract:** This research details a novel methodology for automated graph layout optimization, specifically targeting the challenges of visual clarity and aesthetic appeal in scientific visualizations generated by Prism and Origin. The approach combines Reinforcement Learning (RL) for high-level layout strategy with Dynamic Constraint Satisfaction (DCS) for precise node positioning, resulting in a significantly improved, adaptable algorithm capable of generating layouts that are both structurally sound and visually compelling for datasets that would challenge conventional forced-directed algorithms. This innovation offers a direct pathway to enhanced data comprehension and a more intuitive user experience within the Prism/Origin ecosystem, potentially impacting research workflows across numerous scientific disciplines.