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之一，但早期AI智能体主要聚焦于受限场景，因为实现人类级规划所需的诸多认知基础一直缺失。近年来，由大语言模型驱动的语言智能体展现出工具使用与推理等有趣能力。这些语言智能体能否在超越先前AI智能体能力范围的更复杂环境中进行规划？为推进这一研究，我们提出TravelPlanner——一个聚焦旅行规划这一常见真实世界规划场景的新型规划基准。该基准提供丰富的沙盒环境、访问近四百万数据记录的多类工具，以及1,225个精心策划的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如此复杂的规划任务——即便是GPT-4也仅实现0.6%的成功率。语言智能体难以保持任务专注、正确使用工具收集信息，或同时追踪多重约束条件。但我们注意到，语言智能体能够尝试攻克此类复杂问题本身已是非平凡进展。TravelPlanner为未来语言智能体提供了兼具挑战性与实用性的测试平台。