基于用户偏好动态优化城市路径规划，交互更直观，效率更高。
Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
- 用户可实时反馈优化目标，引导系统迭代生成更符合需求的路线。
- 避免计算完整帕累托前沿，显著降低计算开销和等待时间。
- 特别适合对交互效率和个性化路线有要求的残障人士或特殊需求者。
我们提出了一种面向不同无障碍需求与偏好的城市路径规划方法——偏好引导的迭代帕累托参考优化（PG-IPRO）。该算法允许用户通过反馈来调整路线，例如指出应进一步最小化哪个目标，或放松某个约束，从而实现直观的交互体验。相比基于信息增益的交互方式，这种反馈在早期迭代中更为有效。由于算法的迭代特性，无需计算完整的帕累托前沿，显著提升了计算效率并缩短了用户等待时间。
原文摘要 · Abstract (English)
We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With this algorithm the user can interact with the system by giving feedback on a route, i.e., the user can say which objective should be further minimized, or conversely can be relaxed. This leads to intuitive user interaction, that is especially effective during early iterations compared to information-gain-based interaction. Furthermore, due to PG-IPRO's iterative nature, the full set of alternative, possibly optimal policies (the Pareto front), is never computed, leading to higher computational efficiency and shorter waiting times for users.
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