Abstract
This paper presents preliminary work on integrating symbolic learning and reasoning into autonomous maritime systems using inductive logic programming (ILP). A key challenge in operationalising ILP is bridging the gap between continuous sensing and actuation data and discrete symbolic logic. We propose a framework that enables autonomous vessels to query maritime rules (COLREGs) and learn from human oversight. Using the ILP system PyGol, we demonstrate the learning of COLREG Rule 13 for overtaking situations from discretised bearing data, and further explore the learning of an exception to Rule 15 for crossing situations through examples inspired by case law. These results show the potential for interpretable, legally compliant decision-making and lay the groundwork for learning more complex rules in dynamic maritime environments.