Daniel Borrajo, Giuseppe Canonaco, Tomás de la Rosa, Alfredo Garrachón, Sriram Gopalakrishnan, Simerjot Kaur, Marianela Morales, Sunandita Patra, Alberto Pozanco, Keshav Ramani, Charese Smiley, Pietro Totis, Manuela Veloso

Classical AI Planning techniques generate sequences of actions for complex tasks. However, they lack the ability to understand planning tasks when provided using natural language. The advent of Large Language Models (LLMs) has introduced novel capabilities in human-computer interaction. In the context of planning tasks, LLMs have shown to be particularly good in interpreting human intents among other uses. This paper introduces GenPlanX that integrates LLMs for natural language-based description of planning tasks, with a classical AI planning engine, alongside an execution and monitoring framework. We demonstrate the efficacy of GenPlanX in assisting users with office-related tasks, highlighting its potential to streamline workflows and enhance productivity through seamless human-AI collaboration.