Publikation
Logic Programming for Developing an Interactive Explainable Naval Decision Support System
Tarek Elmihoub; Christoph Michael Tholen; Lars Nolle; Frederic Theodor Stahl
In: Maryam Alimardani; Tom Lenaerts; André Meyer-Vitali; Joost Vennekens Ann Nowé; Shenghui Wang (Hrsg.). Hybrid Human Artificial Intelligence (HHAI) 2026. International Conference on Hybrid Human-Artificial Intelligence (HHAI-2026), The 5th International Conference on Hybrid Human-Artificial Intelligence, located at HHAI-2026, July 6-10, Brussels, Belgium, Pages 36-49, Frontiers in Artificial I, Vol. 423, ISBN 978-1-64368-670-7, IOS Press, Amsterdam, 7/2026.
Zusammenfassung
The increasing complexity of naval traffic necessitates robust decision support systems that enhance safety through compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). This paper proposes using logic programming to develop explainable decision support systems tailored for encounter situations in maritime navigation. Logic programming is utilized to provide recommendations with clear, human-understandable explanations. The framework addresses various encounter scenarios by encoding the COLREGs rules into logical constructs to conclude appropriate actions and facilitates automated reasoning, ensuring rapid and accurate response. Our system is designed to collect sensor data, assess risk levels, and recommend optimal actions that comply with maritime regulations. The inherent explainability of logic programming allows generating traceable and transparent decision paths. Mariners can comprehend the underlying rationale that supports trusted decision-making onboard. The paper explores the implementation of this system, detailing its architecture, inference mechanisms, and integration with Automatic Identification System (AIS) messages. An Interactive eXplainable Decision Support System (IXDSS) was developed to demonstrate its effectiveness in diverse traffic scenarios while providing logical and visual explanations, including interactive ‘what-if’ analyses. IXDSS was evaluated using a standardized set of encounter scenarios, showing accurate scenario prediction, and its explainability was assessed qualitatively.