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This paper introduces the AI Tour Meeting framework, which utilizes multiple LLM-based agents with distinct personas to collaboratively plan group travel itineraries through natural language discussions. By enabling flexible orchestration of agent interactions and providing tools for monitoring and configuration, the framework serves as a simulation tool for analyzing agent behavior in travel planning contexts. Key results validate the framework's effectiveness in facilitating meaningful discussions among agents, revealing insights into their collaborative decision-making processes.
LLM agents equipped with unique personas can collaboratively navigate complex travel planning discussions, revealing surprising dynamics in group decision-making.
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.
