A Modeling Method of Large Language Model-based Decision-making Agents for Maritime Search and Rescue

Current maritime search and rescue (SAR) decision-making faces challenges in unified semantic modeling for multi-source heterogeneous information. This deficiency hinders joint reasoning across cross-modal data. Meanwhile, SAR missions involve multi-objective constraints with significant coupling between disparate decision goals. Traditional methods often rely on stage-wise or heuristic-driven processing. They lack the capacity for holistic reasoning and dynamic optimization within the multi-objective decision-making process.To address these challenges, this paper investigates a modeling methodology for the intelligent search and rescue agent (IS-RescueAgent), This agent utilizes a large language model (LLM) as the core reasoning engine. By implementing the model context protocol (MCP), the framework achieves semantic-level coordination of cross-modal information. It executes closed-loop reasoning and multi-objective optimization within a unified process. Through collaborative mechanisms like problem identification and information parsing, the agent achieves fusion of cross-modal data.This includes distress messages, environmental data, and historical records.It also enables dynamic optimization for vessel scheduling, search paths, and task priorities.Architecturally, the proposed agent comprises three stages: First, the information processing stage achieves unified representation of cross-modal features through semantic extraction and intent recognition.Second, The strategy generation stage constructs a multi-objective optimization framework for scenario-adaptive plan generation. Finally, the learning stage establishes an iterative mechanism through performance feedback and parameter updates.Experimental results show that IS-RescueAgent achieves a relevance of 93%, an accuracy of 92%, and a rationality of 92.3%. These metrics outperform traditional LLM and Retrieval-Augmented Generation (RAG) approaches. Furthermore, the system demonstrates strong stability under environmental disturbances. Under conditions of wind speeds below 10 m/s and resource availability above 70%, error rates remain within 5%. The system also maintains high response efficiency with positioning errors under 10 m and communication delays below 200 ms.