GenPlanX. Generation of Plans and Execution
Daniel Borrajo, Giuseppe Canonaco, Tom\'as de la Rosa, Alfredo Garrach\'on, Sriram Gopalakrishnan, Simerjot Kaur, Marianela Morales, Sunandita Patra, Alberto Pozanco, Keshav Ramani, Charese Smiley, Pietro Totis, Manuela Veloso

TL;DR
GenPlanX combines large language models with classical AI planning to interpret natural language task descriptions, generate plans, and monitor execution, improving human-AI collaboration for office tasks.
Contribution
This paper introduces GenPlanX, a novel framework integrating LLMs with classical planning and execution monitoring for natural language-based planning.
Findings
Effective interpretation of natural language planning tasks
Enhanced workflow efficiency in office scenarios
Seamless human-AI collaboration demonstrated
Abstract
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.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Speech and dialogue systems · Multi-Agent Systems and Negotiation
