AI 中文总结
研究针对旅行规划需平衡多目标约束，当前AI工具支持有限问题，提出AlterAtlas系统，通过基于角色模拟实现行程验证与修订，经实验证明可提高计划与角色匹配度，增强用户对最终计划的信任。
AI 中文摘要
旅行规划需要在时间和空间上平衡相互作用的目标和约束。当前的人工智能旅行工具在编码这些约束以及理解生成的旅行计划如何可能让用户失望方面提供的支持有限。我们提出了AlterAtlas，一个交互式旅行规划系统，它通过基于地理空间信息的基于角色的模拟来支持高保真行程验证和修订。AlterAtlas将旅行者建模为可编辑的角色，从优先考虑的感兴趣地点生成候选行程，并模拟不同角色将如何体验每个计划。模拟揭示了路线级的权衡、时间上的用户状态（如疲劳、饥饿）以及计划与用户偏好之间的不匹配，从而允许用户迭代地完善行程和用户角色。对51对行程的专家评估表明，模拟引导的修订显著提高了计划与角色的匹配度。此外，一项受试者内研究（N = 11）表明，AlterAtlas使用户能够发现隐藏的约束，流畅地比较替代方案，并对最终计划建立信任。我们的结果表明，基于模拟的验证是人工智能辅助旅行规划的一个强大、透明的交互层。
英文摘要
Travel planning requires balancing interacting goals and constraints across time and space. Current AI travel tools provide limited support for encoding these constraints and understanding how generated travel plans may fail users. We present AlterAtlas, an interactive travel planning system that supports high-fidelity itinerary validation and revision through persona-based simulations grounded in geospatial information. AlterAtlas models travelers as editable personas, generates candidate itineraries from prioritized places of interest, and simulates how different personas would experience each plan. Simulations expose route-level tradeoffs, temporal user states (e.g., fatigue, hunger), and mismatches between plans and user preferences to allow users to iteratively refine both itineraries and user personas. An expert evaluation of 51 paired itineraries demonstrates that simulation-guided revisions significantly improve plan-persona alignment. Furthermore, a within-subjects study (N=11) reveals that AlterAtlas empowers users to uncover hidden constraints, fluidly compare alternatives, and build trust in their final plans. Our results suggest that simulation-based validation is a powerful, transparent interaction layer for AI-assisted travel planning.
Comments11 pages, 8 figures. For associated codebase, see https://github.com/hilab-open-source/alteratlas
