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MarineLLM-PDDL: Generation of Planning Domains for Marine Vessels Using Past Incident Response Plans

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Research
Peer-review

Open access

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.

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Research
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 307-313 (7 pages)

Publication milestones

  • Accepted/In press - 24/02/2025
  • Published - 2025

Publication status

Published - 2025

Place of publication

Springer Cham

Volume

36

Publisher

Springer, United States, Germany

Book series

  • Book series name: European Robotics Forum
978-3-031-89470-1

ISBN (Electronic)

978-3-031-89471-8

Publication IDs

  • Scopus: 105006477259

Host publication title

Springer Proceedings in Advanced Robotics

Publication metrics

Related Event

Title

Conference on European Robotics Forum

Event type

Conference

Degree of recognition

International event

Date

25/03/2025 - 27/03/2025

Location

Kultur- & Kongresszentrum LiederhalleStuttgartGermany