@inproceedings{78ed94dd58f34e94a723b8207de56878,
title = "MarineLLM-PDDL: Generation of Planning Domains for Marine Vessels Using Past Incident Response Plans",
abstract = "Testing the hardware and software of marine vessels in field trials is a necessity to avoid technical and environmental catastrophes. Conducting tests with large vessels is costly. Multiple realistic domain descriptions based on past missions could increase the value of simulation tests, reducing the need for expensive field tests. In this paper, we generate scenarios from unstructured Incident Response Plan (IRP) documents using Large Language Models (LLMs), converting them to standard structured planning programs. The two synthesized marine test-domain datasets contain approximately 90\% parsable, 75\% solvable, and 57\% correct planning programs.",
keywords = "Mission Planning, Marine Vessels, Large Language Models (LLMs), Scenarios Generation, Test Domain",
author = "\{Mohammadi Kashani\}, Mahya and Stefan Heinrich and Andrzej Wasowski",
year = "2025",
doi = "10.1007/978-3-031-89471-8\_47",
language = "English",
isbn = "978-3-031-89470-1",
volume = "36",
series = "European Robotics Forum",
publisher = "Springer",
pages = "307--313",
booktitle = "Springer Proceedings in Advanced Robotics",
address = "Germany",
}