  Recent studies have highlighted their proficiency in some simple tasks like
writing and coding through various reasoning strategies. However, LLM agents
still struggle with tasks that require comprehensive planning, a process that
challenges current models and remains a critical research issue. In this study,
we concentrate on travel planning, a Multi-Phases planning problem, that
involves multiple interconnected stages, such as outlining, information
gathering, and planning, often characterized by the need to manage various
constraints and uncertainties. Existing reasoning approaches have struggled to
effectively address this complex task. Our research aims to address this
challenge by developing a human-like planning framework for LLM agents, i.e.,
guiding the LLM agent to simulate various steps that humans take when solving
Multi-Phases problems. Specifically, we implement several strategies to enable
LLM agents to generate a coherent outline for each travel query, mirroring
human planning patterns. Additionally, we integrate Strategy Block and
Knowledge Block into our framework: Strategy Block facilitates information
collection, while Knowledge Block provides essential information for detailed
planning. Through our extensive experiments, we demonstrate that our framework
significantly improves the planning capabilities of LLM agents, enabling them
to tackle the travel planning task with improved efficiency and effectiveness.
Our experimental results showcase the exceptional performance of the proposed
framework; when combined with GPT-4-Turbo, it attains $10\times$ the
performance gains in comparison to the baseline framework deployed on
GPT-4-Turbo.
