Decoupled Travel Planning with Behavior Forest

2026-04-23

AI Summary

This work addresses the challenge in multi-constrained travel planning where the coupling of local and global constraints leads to high decision complexity and substantial reasoning overhead. To mitigate this, the authors propose a “Behavior Forest” architecture that decomposes the planning task into multiple parallel behavior trees, each handling a specific subtask, while a global coordination mechanism enables modular collaboration among them. This approach explicitly decouples local and global constraints and integrates large language models (LLMs) with the structured control flow of behavior trees, thereby reducing the cognitive load on the LLM and enhancing planning efficiency. Evaluated on the TravelPlanner and ChinaTravel benchmarks, the method outperforms the current state-of-the-art by 6.67% and 11.82%, respectively.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning step is not only constrained locally but also influenced by global constraints spanning multiple subtasks, leading to a tightly coupled and complex decision process. Existing travel planning methods typically rely on a single decision space that entangles all subtasks and constraints, failing to distinguish between locally acting constraints within a subtask and global constraints that span multiple subtasks. Consequently, the model is forced to jointly reason over local and global constraints at each decision step, increasing the reasoning burden and reducing planning efficiency. To address this problem, we propose the Behavior Forest method. Specifically, our approach structures the decision-making process into a forest of parallel behavior trees, where each behavior tree is responsible for a subtask. A global coordination mechanism is introduced to orchestrate the interactions among these trees, enabling modular and coherent travel planning. Within this framework, large language models are embedded as decision engines within behavior tree nodes, performing localized reasoning conditioned on task-specific constraints to generate candidate subplans and adapt decisions based on coordination feedback. The behavior trees, in turn, provide an explicit control structure that guides LLM generation. This design decouples complex tasks and constraints into manageable subspaces, enabling task-specific reasoning and reducing the cognitive load of LLM. Experimental results show that our method outperforms state-of-the-art methods by 6.67% on the TravelPlanner and by 11.82% on the ChinaTravel benchmarks, demonstrating its effectiveness in increasing LLM performance for complex multi-constraint travel planning.

Problem

• travel planning
• multi-constraint planning
• behavior sequences
• decision decoupling
• global constraints

Innovation

• Behavior Forest
• Decoupled Planning
• Behavior Trees
• Large Language Models
• Multi-constraint Travel Planning