Date of Award

Winter 2023

Project Type


Program or Major

Computer Science

Degree Name

Master of Science

First Advisor

Samuel Carton

Second Advisor

Samuel Carton

Third Advisor

Momotaz Begum


A common scenario in robotics is needing to change the behavior of a deployed robot according to new requirements. This usually requires intervention from the creators of the robot, which both labor- and time-intensive. In this work, we explore the capability of a large language model (LLM) to address this issue by modifying the existing behavior of robots represented using PDDL. Specifically, we prompt the GPT-4 LLM to generate new PDDL problem files given an existing problem file and a natural language description of how the robot's behavior should change. We evaluate both zero- and one-shot versions of this method on a modified version of an existing PDDL dataset, and compare our results with a baseline approach. We find that this problem is challenging for LLM to solve and discuss possibilities for improving performance.