GenPlanX. Integrating LLMs and Classical AI for Generation of Plans and Execution

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

Keywords: AI Classical Planning, LLM Planning, Hybrid AI

TL;DR: GenPlanX integrates LLMs for natural language-based description of planning tasks, with a classical AI planning engine

Abstract: AI Planning techniques generate sequences of actions for complex tasks. However, they lack the ability to understand planning tasks when provided using natural language. Now, Large Language Models (LLMs) provide 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.

Submission Type: Research Paper (4-9 Pages)

Submission Number: 18