🤖 AI Summary
This work addresses the challenge of reconciling diverse preferences and constraints in group travel planning by proposing the first collaborative planning framework based on multiple large language model (LLM) agents. The framework assigns distinct roles to LLMs and leverages natural language dialogue to facilitate negotiation, while incorporating a configurable workflow orchestrator to enable automatic itinerary generation and real-time monitoring and analysis of the discussion process. Experimental results demonstrate the framework’s effectiveness, uncovering behavioral patterns and decision dynamics among multi-agent negotiations. This approach establishes a novel paradigm for modeling group decision-making processes in complex, preference-sensitive domains such as collective travel planning.
📝 Abstract
This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents. The agents are instantiated with distinct personas and collaboratively seek an itinerary that satisfies their constraints and preferences through natural language discussion. The framework enables easy and flexible orchestration of such discussions by providing interfaces for configuring agent personas, discussion workflows, monitoring, and LLM deployment. Its primary use case is a simulation tool for analyzing the behavior of multiple LLM agents during tour planning discussions. This paper demonstrates the utility of the framework by presenting system validation and several analytical results obtained by the framework.