TravelAgent：一款用于个性化旅行规划的AI助手
TravelAgent: An AI Assistant for Personalized Travel Planning
AI总结：
针对现有旅行规划系统难以兼顾合理性、全面性与个性化的问题，提出由大语言模型驱动、含四大模块的TravelAgent系统，经人类与模拟用户验证，其在三项核心标准上表现优异，个性化推荐准确。
摘要：
随着全球旅游业扩张与人工智能技术进步，智能旅行规划服务已成为重要研究方向。在存在多维度约束的动态真实旅行场景中，支持用户自动生成实用定制旅行行程的服务需满足三大核心目标：合理性、全面性与个性化。然而，现有采用规则组合或基于大语言模型（LLM）规划方法的系统难以完全满足这些标准。为应对上述挑战，我们提出TravelAgent——一款由大语言模型驱动的旅行规划系统，旨在基于动态场景提供合理、全面且个性化的旅行行程。TravelAgent包含四个模块：工具使用模块、推荐模块、规划模块与记忆模块。我们通过人类用户和模拟用户评估了TravelAgent的性能，证明其在三项标准上的整体有效性，并验证了个性化推荐的准确性。
英文摘要：
As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
