  Travel planning is a complex task that involves generating a sequence of
actions related to visiting places subject to constraints and maximizing some
user satisfaction criteria. Traditional approaches rely on problem formulation
in a given formal language, extracting relevant travel information from web
sources, and use an adequate problem solver to generate a valid solution. As an
alternative, recent Large Language Model (LLM) based approaches directly output
plans from user requests using language. Although LLMs possess extensive travel
domain knowledge and provide high-level information like points of interest and
potential routes, current state-of-the-art models often generate plans that
lack coherence, fail to satisfy constraints fully, and do not guarantee the
generation of high-quality solutions. We propose TRIP-PAL, a hybrid method that
combines the strengths of LLMs and automated planners, where (i) LLMs get and
translate travel information and user information into data structures that can
be fed into planners; and (ii) automated planners generate travel plans that
guarantee constraint satisfaction and optimize for users' utility. Our
experiments across various travel scenarios show that TRIP-PAL outperforms an
LLM when generating travel plans.
