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.
@article{arxiv.2506.10897,
title = {GenPlanX. Generation of Plans and Execution},
author = {Daniel Borrajo and Giuseppe Canonaco and Tomás de la Rosa and Alfredo Garrachón and Sriram Gopalakrishnan and Simerjot Kaur and Marianela Morales and Sunandita Patra and Alberto Pozanco and Keshav Ramani and Charese Smiley and Pietro Totis and Manuela Veloso},
journal= {arXiv preprint arXiv:2506.10897},
year = {2025}
}