Abstract
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.