  In recent years, large language models (LLMs) have rapidly proliferated and
have been utilized in various tasks, including research in dialogue systems. We
aimed to construct a system that not only leverages the flexible conversational
abilities of LLMs but also their advanced planning capabilities to reduce the
speaking load on human interlocutors and efficiently plan trips. Furthermore,
we propose a method that divides the complex task of a travel agency into
multiple subtasks, managing each as a separate phase to effectively accomplish
the task. Our proposed system confirmed a certain level of success by achieving
fourth place in the Dialogue Robot Competition 2023 preliminaries rounds. We
report on the challenges identified through the competition.
