Planning has been part of the core pursuit for artificial intelligence since its conception, but earlier AI agents mostly focused on constrained settings because many of the cognitive substrates necessary for human-level planning have been lacking. Recently, language agents powered by large language models (LLMs) have shown interesting capabilities such as tool use and reasoning. Are these language agents capable of planning in more complex settings that are out of the reach of prior AI agents? To advance this investigation, we propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans. Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%. Language agents struggle to stay on task, use the right tools to collect information, or keep track of multiple constraints. However, we note that the mere possibility for language agents to tackle such a complex problem is in itself non-trivial progress. TravelPlanner provides a challenging yet meaningful testbed for future language agents.

翻译：规划自人工智能诞生之初便是其核心追求，但早期的AI智能体大多聚焦于受约束场景，因为实现类人规划所需的认知基础长期缺失。近期，基于大语言模型的语言智能体展现出工具使用与推理等令人瞩目的能力。这些语言智能体能否在超越以往AI智能体能力范围的复杂场景中完成规划？为推进这一研究，我们提出TravelPlanner——一个聚焦于旅行规划这一常见真实世界规划场景的新型基准测试。该基准提供了丰富的沙盒环境、用于获取近四百万条数据记录的多类工具，以及精心设计的1225个规划意图与参考方案。全面评估表明，当前语言智能体尚无法处理此类复杂规划任务——即便GPT-4的成功率也仅为0.6%。语言智能体在任务保持、选择恰当工具获取信息、以及追踪多重约束方面存在显著困难。但我们注意到，语言智能体能够尝试解决此类复杂问题本身已是非平凡进展。TravelPlanner为未来语言智能体研究提供了兼具挑战性与价值性的测试平台。