CAPTAIMN: A Real-Time LLM and RAG Based Decision Support System for Navigational Safety and COLREGs Compliance

Abstract

The correct regulatory interpretation in naval environments is challenging due to the complexity and urgency of decisions based on the International Regulations for Preventing Collisions at Sea (COLREGs). This article presents the development of an intelligent agent named Cognitive Agent for Analysis of Interrelated Problems in Maritime Navigation, hereafter referred to as CAPTAIMN. This agent integrates Large Language Models (LLM) and a Retrieval-Augmented Generation (RAG) architecture to support human decision-making and officer training in safety-critical naval environments. Thus, this work aims to propose a methodology for building an intelligent agent based on LLM and RAG, specifically focused on the assisted and contextualized interpretation of COLREGs. The proposed methodology was evaluated through a quantitative study with 15 maneuvering officers, who assessed 150 responses generated by a local language model using a Likert scale. The results from this phase showed significant approval, with 80% of the responses being rated as 'Agree' or 'Totally Agree' by the officers. These results suggest that the integration of LLM and RAG through CAPTAIMN can provide useful support for both decision-making and tactical training in naval operations.