Shubhi Verma

Maritime Autonomous Surface Ships (MASS) promise to reduce casualty rates and improve operational efficiency, yet two obstacles impede widespread adoption: the qualitative, often conflicting language of the COLREGs and the opacity of prevailing AI collision-avoidance algorithms. We present a socio-technical decision framework that formalises COLREG hierarchy, including the lex specialis ordering confirmed in Ever Smart v. Alexandra 1, as a tiered rule tree and encodes it in an explainable, auditable knowledge base. Using symbolic logic, the system resolves rule conflicts, logs its reasoning, and outputs a single safe manoeuvre aligned with good seamanship. Three representative scenarios (narrowchannel crossing, cascading multi-vessel conflict, and overtaking in a channel) demonstrate that the framework reproduces expert decisions while exposing a transparent proof trail. The result is a legally coherent foundation for logic-based machine learning using inductive logic programming (ILP) and future maritime autonomous systems trials, advancing the IMO goal of "at least equivalent" safety for unmanned vessels.

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.