AI Knowledge Agents in Maritime Operations

TLDR

The maritime industry is going through a significant digital shift, leading to a large amount of operational, logistical, and regulatory data. This increase in data often exceeds our ability to analyze it in real-time, creating challenges for safety and efficiency.

Knowledge agents are a type of Artificial Intelligence (AI) that can think, plan, analyze, and act independently. They have the potential to help solve these challenges. This report looks at how knowledge agents can be used in the maritime industry and the obstacles to their implementation. It targets technology professionals who need insights into strategy and technology.

Knowledge agents can effectively process and analyze large and diverse sets of data. They can improve operational efficiency by optimizing voyage planning, adjusting routes dynamically, coordinating logistics better, and simplifying port interactions. They can also enhance safety by identifying risks early, predicting maintenance needs, detecting problems, and supporting decisions that help avoid collisions. This reduces the risks that come from human cognitive limits in high-pressure situations. Furthermore, these agents can uncover valuable insights from complex data, enabling better trend analysis and forecasts, while also helping with regulatory compliance.

However, several challenges stand in the way of widespread use. Managing large amounts of data, its volume, speed, variety, and accuracy, remains a major issue. Analyzing data from multiple sources in real time is a complex technical task. To gain user trust and meet regulations, it is crucial that knowledge agents' decisions are transparent and easy to explain. This is especially important when safety is involved. Integrating these agents with older systems can also be difficult. We need to establish effective ways for humans and AI to work together and ensure proper oversight for safe and effective use. Additionally, we must tackle cybersecurity risks and close the skills gap among maritime workers to implement these systems successfully.

Maritime stakeholders should develop strong data governance strategies and focus on improving the quality and standardization of data. It's important to prioritize the development of easy-to-understand AI techniques to build trust. Investments should be made in advanced AI models and scalable data infrastructure, like geospatial databases and real-time processing systems. The design of these agents should center on our needs, emphasizing collaboration and user-friendly interfaces. We also need to prepare employees for these changes through training and skill development. Finally, working together within the industry and with regulatory bodies while supporting pilot projects is crucial. This will help navigate the challenges of adoption and ensure that knowledge agents become valuable tools for improving safety, efficiency, and intelligence in global maritime operations.

1. Introduction

1.1 The Data Overflow

The modern maritime industry generates a huge amount of data, more than 100 million data points every day. This data comes from many sources, such as real-time Automatic Identification System (AIS) feeds that show vessel positions every 2 to 10 seconds, continuous sensor data monitoring engine performance, fuel use, and emissions, as well as weather forecasts, port schedules, cargo lists, regulatory documents, Vessel Traffic Services (VTS) communications, and maintenance records. The Internet of Ships (IoS) further increases this data generation by connecting different ship systems and devices.

While this data can be very useful, its large volume and variety create a problem known as information overload. Maritime professionals, like operation managers, logistics coordinators, safety officers, and captains, often find it hard to keep up with data streams that exceed their ability to process and analyze information in real-time. This issue worsens due to problems with data quality, including delays, lack of accuracy, inconsistent standards, and manipulation risks. These factors lead to lower operating efficiency and can negatively impact safety. Research shows that a large percentage of maritime accidents, between 75% and 96%, are caused by human factors, such as information overload, fatigue, and poor decision-making under pressure.

Maritime Informatics is a new field that aims to help people use digital data to improve maritime operations, safety, sustainability, and resilience. In this context, "data overflow" is a key challenge. The issue is not just about having too much data but also its complexity, including different types of data, real-time demands, varying standards, and reliability issues. This complexity limits the effectiveness of standard human analysis and basic analytical tools, causing a gap between data availability and useful insights. Additionally, the industry's ongoing digitalization can worsen the information overload problem in the short term. Early digital systems often add more data streams and interfaces without providing proper analysis tools, increasing cognitive load before more advanced AI solutions are available. This situation highlights the urgent need for technology that can effectively manage and interpret this complex information.

1.2 Knowledge Agents

AI agents are software entities that observe their surroundings using sensors or data inputs. They can make decisions and take actions to meet specific goals while learning from their experiences. What sets them apart is their ability to actively pursue objectives instead of just reacting to questions or following strict instructions.

The term "knowledge agent," along with related terms like "operational agent" or "research agent" in maritime contexts, refers to a special type of AI agent that functions well in complex fields like the maritime industry. These agents can process, analyze, and reason with large amounts of information specific to their field, drawn from various sources.

Key characteristics that define knowledge agents include:

• Autonomy: The capacity to operate independently, making plans and taking actions without constant human intervention.
• Goal-Orientation: Driven by objectives defined by human users or organizational needs.
• Planning and Reasoning: The ability to analyze situations, develop strategies, break down complex tasks into sub-steps, and use logic to determine the best course of action.
• Tool Use: The capability to interact with and leverage external systems, databases, APIs, or other AI models to gather information or perform specific functions.
• Memory and Context: The ability to retain information across tasks and interactions, maintaining context to inform future decisions.
• Learning and Adaptation: The capacity to improve performance over time-based on experience, feedback, and new data.

Knowledge agents work differently from simple automation tools like chatbots, which usually follow set scripts, or rule-based systems that can't adapt. In the maritime industry, a knowledge agent can analyze real-time AIS data, weather reports, regulatory information, and vessel performance data. It can plan the best route for a journey, spot possible collision risks by considering vessel movements and environmental factors, check for hazards using a geospatial database, look up compliance information with a knowledge graph, and learn from the results of its suggestions to improve future plans.

The main strength of knowledge agents, especially important in dealing with maritime data, is their ability to analyze information from many different and changing sources. They strive to create a clear understanding of the operational environment, enabling them to do meaningful work beyond basic data handling or task execution. This analytical skill helps them manage the complexity and variety of data better than simpler tools can. Additionally, these agents can use external tools, which is crucial. The industry depends on specialized systems like weather forecast APIs, regulatory databases, vessel knowledge graphs, and geospatial databases. Agents that can quickly query these specialized tools are much more flexible and powerful than systems that try to keep all information in one place. This setup allows them to access the best available information and analytical tools for specific tasks like voyage planning or checking compliance.

1.3 Potential for Transformation

The introduction of knowledge agents can greatly improve maritime operations, possibly more than earlier digital tools like AIS and ECDIS.

AIS, introduced by the International Maritime Organization (IMO) in the early 2000s, improved awareness by automatically sending out a vessel’s identity, position, course, and speed. This helped prevent collisions and provided important data for analyzing traffic and investigating incidents. ECDIS further advanced navigation by replacing paper charts with digital displays and combining electronic navigational charts (ENCs) with GPS and radar data. This made navigation more accurate, reduced manual work, and increased safety with real-time positioning and alerts.

Knowledge agents take this evolution further by moving from just showing data to interpreting it and taking action. While AIS and ECDIS focus on making data available and increasing awareness, knowledge agents automate data analysis, spot patterns, predict future events, and suggest the best actions to take.

This change promises to improve efficiency, like optimizing routes to save fuel, and enhance safety through proactive risk management. Knowledge agents can also check compliance automatically. They analyze data from many vessels and ports, allowing for system-wide optimization that wasn't possible before. They can manage fleet movements, predict congestion, and improve visibility in supply chains.

Additionally, knowledge agents continuously learn and adapt their strategies based on new data and user feedback. This ongoing improvement suggests long-term gains in efficiency and safety, moving from one-time updates to a system that keeps evolving intelligently.

2. Challenges with Knowledge Agents

Knowledge agents offer a powerful approach to tackling long-standing and emerging challenges within the industry, primarily by leveraging their ability to process, analyse, and reason over vast and complex datasets. Their application spans a wide range of critical workflows.

2.1 Applications

• Voyage Planning & Optimization: Agents can ingest a multitude of variables including dynamic weather forecasts, sea conditions, vessel-specific fuel efficiency models, real-time AIS traffic data, port schedules and congestion levels, cargo requirements, vessel characteristics, and complex regulatory constraints to compute and propose optimal routes. This optimization can balance competing objectives such as minimizing transit time, reducing fuel consumption and emissions, ensuring safety margins, and maximizing the probability of meeting required ETAs. Furthermore, agents can continuously monitor conditions and dynamically recommend or execute route adjustments in response to real-time changes.
• Risk Assessment & Mitigation: By analyzing historical incident and near-miss data, real-time sensor feeds indicating equipment health, weather hazard information, traffic density patterns, geospatial data layers (e.g., bathymetry, sensitive areas), and compliance status, agents can proactively identify and assess a wide range of operational risks. This includes predicting the likelihood of collisions, groundings, equipment failures (predictive maintenance), security threats (e.g., piracy zones, suspicious vessel behavior), and potential compliance breaches. This allows for timely implementation of preventive measures.
• Safety Analysis & Monitoring: Agents can continuously monitor ongoing operations against established safety management system (SMS) procedures and best practices. They can detect anomalies in vessel behavior that might indicate distress, illicit activity, or navigational errors, such as unexpected deviations from planned routes or inconsistencies suggestive of AIS manipulation. Where data is available (e.g., bridge audio/video, crew wearables), agents could potentially monitor for indicators of crew fatigue. Post-incident, agents can assist investigations by rapidly reconstructing events using fused data sources.
• Logistics Coordination & Supply Chain Visibility: Knowledge agents can analyze data across the supply chain, integrating cargo manifests, real-time vessel tracking and ETA predictions, port status updates and congestion forecasts, customs documentation, and data from connecting intermodal transport legs. This analysis view can provide unprecedented end-to-end supply chain visibility, enabling proactive alerting for potential delays, optimization of cargo handling operations, and improved coordination between vessels, ports, and land-based logistics providers.
• Regulatory Compliance: Agents can be programmed with knowledge of international conventions (IMO, SOLAS, MARPOL) and local regulations. They can automatically audit voyage plans, cargo documentation (e.g., hazardous material segregation), emissions logs, and operational procedures against these rules, flagging potential non-compliance issues before they lead to penalties or detentions.
• Port Operations Optimization: Agents can serve port authorities and terminal operators by analyzing real-time vessel traffic, weather conditions, and resource availability. They can predict vessel arrival and departure times with greater accuracy, optimize berth allocation strategies, manage traffic flow within port limits to minimize congestion, forecast bottlenecks, and streamline the allocation of essential resources like pilots, tugboats, and cranes.

Across these diverse applications, the unique value of knowledge agents lies in their capacity for cross-domain analysis. Optimizing a voyage, for instance, is not merely a weather routing problem; it requires simultaneously considering fuel economics, vessel performance characteristics, real-time traffic, predicted port congestion, specific cargo requirements, crewing factors, and a complex web of regulations. Agents, capable of processing and reasoning across these disparate factors concurrently, can identify solutions that are closer to a global optimum, surpassing the capabilities of humans or specialized tools focused on isolated aspects of the problem.

A recurring theme in these applications is the move towards predictive capabilities. Predictive maintenance aims to prevent failures rather than react to them. Risk assessment focuses on predicting and mitigating potential incidents before they occur. Logistics applications involve predicting delays and ETAs to enable proactive adjustments. This signifies a fundamental shift from traditional reactive operational management, based largely on current conditions, towards proactive, forward-looking strategies informed by AI-driven predictions about future states.

2.2 Current Landscape

The current maritime technology market offers a range of software solutions, many incorporating elements of AI and data analytics. However, true knowledge agents, as defined earlier, represent a significant evolution beyond most existing platforms.

Existing Tools and Platforms:

• Fleet Management Software (FMS): Platforms like SeaLogs, Helm CONNECT, IFS, typically provide core functionalities for vessel tracking, basic operational reporting, planned maintenance scheduling, inventory management, and logging compliance data. While increasingly digital, they often rely on manual data entry or basic sensor integration and possess limited capabilities for advanced analytics, cross-domain data synthesis, or autonomous decision support.
• Vessel Tracking Platforms: Services such as MarineTraffic, VesselFinder, and FleetMon excel at aggregating and displaying real-time or near real-time AIS data, offering vessel positions, historical tracks, port call information, and basic alerting functions. Their primary focus is situational awareness based on vessel movements, often lacking deep integration with other critical data types like weather, cargo specifics, or detailed vessel performance metrics for comprehensive analysis or prediction.
• Specialized AI/Analytics Tools: A growing number of solutions leverage AI/ML for specific maritime challenges. Examples include dedicated weather routing services, fuel consumption optimization platforms, predictive maintenance systems, port congestion prediction APIs, and risk analysis platforms using AI to assess vessel behavior or compliance (e.g., Windward, Orca AI for navigation assistance). While powerful in their niche, these tools often operate in silos, addressing only a specific part of the larger operational puzzle.

Knowledge Agent Differentiation: Knowledge agents distinguish themselves from these existing tools in several key aspects:

• Scope & Analysis: Agents aim for a cross-functional perspective, performing deep analysis across diverse data types (weather, cargo, regulations, performance, traffic, etc.) to build a comprehensive operational picture. This contrasts with the often siloed, task-specific nature of current tools or the basic data aggregation in FMS/tracking platforms.
• Autonomy & Reasoning: Agents possess higher levels of autonomy, capable of proactive planning, complex reasoning, sophisticated decision support, and potentially initiating actions based on their analysis. This goes beyond the primarily informational or rule-based alert functions of most current systems.
• Learning & Adaptation: Agents are designed for continuous learning and adaptation, improving their performance and refining their models based on new data and feedback. This dynamic improvement contrasts with the often static algorithms or infrequent manual updates of many existing tools.
• Explainability (Design Goal): While not always perfectly realized, a key design principle for knowledge agents in critical domains is the need for transparent reasoning (XAI), allowing users to understand why a recommendation or decision was made. This contrasts with the often opaque "black box" nature of algorithms in some current AI-powered tools.
• Adoption Status: While AI is increasingly being applied in the maritime sector through specialized tools and analytics platforms, the concept of the fully autonomous, synthesizing, reasoning knowledge agent described here is still largely emergent. Platforms incorporating advanced data fusion and AI for specific high-value problems like risk assessment (e.g., Windward) or navigation assistance (e.g., Orca AI) represent significant steps along this trajectory but may not yet encompass the full breadth of capabilities envisioned for holistic knowledge agents.

Table 1: Feature Comparison: Knowledge Agents vs. Existing Maritime AI/Analytics Platforms

3. Technical Foundations

Developing and using effective maritime knowledge agents depends on AI techniques and strong data systems that can manage the unique features of the data. However, there are still major technical challenges, especially related to data quality, how data is put together, and the models themselves.

3.1 AI Architectures and Techniques for Maritime Data

Maritime operations generate a rich tapestry of data types, each presenting unique challenges and requiring appropriate AI techniques for processing:

• Geospatial Data: Includes vessel positions (latitude/longitude), planned routes, port boundaries, restricted zones, bathymetry, and environmental sensitivity maps. Requires spatial indexing and querying capabilities.
• Time-Series Data: Encompasses sequential data points recorded over time, such as vessel speed, heading, engine RPM, fuel consumption, vibration levels, weather parameters (wind speed, wave height), and AIS position updates. Requires models adept at capturing temporal dependencies and patterns.
• Textual Data: Consists of unstructured or semi-structured text found in regulations, incident reports, maintenance logs, port circulars, cargo manifests, and email communications. Requires Natural Language Processing (NLP) techniques for interpretation.
• Sensor/Image Data: Includes readings from various onboard sensors (engine temperature, pressure, flow rates), environmental sensors (air/water quality), and potentially visual data from cameras (daylight/thermal) for navigation or monitoring.2 Requires signal processing and computer vision techniques.

Handling these diverse data types effectively is complicated by inherent challenges: massive volume, high velocity (real-time streams), variety in formats and structures, veracity issues (noise, gaps, inaccuracies, deliberate manipulation like AIS spoofing), dynamism (constantly changing conditions), and the need for low-latency processing for real-time decision support.

Several AI/ML architectures are relevant, each with strengths and weaknesses:

• Traditional Machine Learning (ML): Techniques like Random Forests, Support Vector Machines (SVM), XGBoost, and clustering algorithms (such as DBSCAN) work well for specific tasks that use historical data. These techniques help with predictive maintenance, predicting risks like grounding or collisions, clustering vessel trajectories, and estimating emissions. However, these models have difficulty with complex reasoning, planning several steps ahead, and combining information from very different sources.
• Deep Learning (DL): Learning from large datasets can be powerful.
  • Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), and Multi-Layer Perceptrons (MLPs) are basic structures used for various predictions.
  • Convolutional Neural Networks (CNNs) are great for processing data that is organized in grids, especially images from cameras or radar, to find and classify objects.
  • Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) are designed to work with sequences. They are useful for analyzing time-based data like boat paths, sensor data, or weather trends.
  • Transformers and Large Language Models (LLMs) have changed natural language processing (NLP). They help in understanding and creating text. These models can also assist with analyzing rules, reports, and logs. Their reasoning skills may make them valuable for tasks that require knowledge.
  • GeoAI is a new field that combines AI and machine learning with methods for analyzing geographic data. Techniques like Nonlinear Autoregressive with Exogenous Inputs (NARX) networks show promise for predicting geospatial data over time.
• Reinforcement Learning (RL): There is potential to improve decision-making in tasks like planning voyages or avoiding collisions. Reinforcement learning (RL) agents learn by trying different actions and getting feedback, such as rewards or penalties, usually in simulated settings. This method can help find the best solutions by considering many factors at once. It may overcome the limitations of supervised methods that depend on examples of the best outcomes.
• Hybrid Approaches: Because maritime tasks and data are complex and varied, practical knowledge agents will probably use more than one AI approach. Hybrid systems, especially those that use multiple agents, seem to be the best option. These systems could bring together large language models (LLMs) for understanding language, planning, and managing tools, along with specialized machine learning and deep learning models for specific predictions, such as risk, estimated time of arrival (ETA), and equipment failure, or for data processing like image recognition and time-series analysis. They might also include reinforcement learning components to improve certain policies, such as reducing fuel consumption. Frameworks like ReAct (Reason+Act) show how LLMs can work with external tools, including other AI models, to tackle complex problems.

But there are also limitations. Current models often lack robust, general-purpose reasoning capabilities, especially when dealing with unknown situations not well-represented in training data. Achieving high-quality analysis across multiple dynamic, noisy data sources remains a major challenge, even for state-of-the-art models. Furthermore, many powerful models, particularly deep learning architectures, suffer from a lack of interpretability (the "black box" problem), making it difficult to understand why they produce a particular output, which is a critical barrier in safety-conscious domains.

The development of these advanced AI capabilities is fundamentally constrained by the availability, quality, and structure of maritime data. Deep learning models, for example, typically require vast amounts of labeled training data, which can be expensive and time-consuming to create, especially for complex behaviors or rare events. The adage "garbage in, garbage out" holds particularly true; the performance ceiling of any maritime AI model is ultimately determined by the quality of the data it is trained on and operates with.

3.2 Data Infrastructure

The effectiveness of knowledge agents depends on a strong data infrastructure that can store, manage, process, and deliver relevant information quickly and reliably. Key components of this infrastructure include:

• Geospatial Databases: Standard relational databases often struggle with location-based data. Instead, specialized geospatial databases or extensions like PostGIS for PostgreSQL are better at handling this type of information. They offer data types, indexes (like R-trees), and functions that support spatial queries, such as finding vessels in a specific area, calculating distances, and checking how close something is to hazards. Managing large datasets, like global AIS logs, can be challenging because of their size and frequent updates. Additionally, newer vector databases, commonly used in AI for searching similar items, can also help with geospatial data by allowing efficient queries based on learned spatial patterns.
• Knowledge Graphs (KGs): Knowledge Graphs (KGs) are a useful tool for showing complex relationships between different maritime entities, like vessels, ports, companies, regulations, cargo types, and historical incidents. In this structure, nodes represent the entities, while edges show the relationships between them, such as "Vessel A" is owned by "Company B," or "Regulation X" applies to "Vessel Type Y." KGs are good at combining different types of data, linking steady vessel details from registries with changing AIS events, text from regulations, and past incident data. This combination helps agents reason more effectively, such as figuring out whether a vessel complies with rules based on its type, location, and relevant regulations, or understanding the effects of an equipment failure by looking at connected systems. However, building and keeping large-scale maritime KGs updated with real-time data is challenging. Important issues include designing the structure properly, integrating data, managing consistency, and ensuring the system can grow as needed.
• Real-Time Stream Processing: Maritime data, such as AIS, sensors, and weather updates, arrives quickly. Because of this, batch processing is often too slow for timely decision-making. Real-time stream processing tools like Apache Kafka for data collection and Apache Flink or Spark Streaming for processing are important for managing continuous data flows. These systems allow us to analyze, filter, aggregate, detect anomalies, and send alerts as data comes in. This immediate access to information helps decision-makers respond to events like possible collisions or sudden weather changes. Some key challenges include managing delays, ensuring data accuracy, and processing data exactly once within the stream. We also need to integrate stream processing with historical batch data to provide context.

These infrastructure components work together and need to support each other. A knowledge agent handling voyage planning might check a geospatial database for route constraints, look at a knowledge graph (KG) for rules and vessel performance details, and keep an eye on real-time updates for weather and traffic. Integrated platforms, like the VesselAI architecture, aim to bring together these functions—data collection, adding meaning to data (often using KGs), AI modeling, and analytics/visualization—into one cohesive system.

The choice of data infrastructure greatly affects a knowledge agent's abilities and performance in real-world situations. An agent that can only use a simple relational database will find it hard to manage the complex relationships in maritime knowledge compared to one that can use the rich information in a KG. Likewise, an agent without real-time processing can’t give quick updates in fast-changing situations. Therefore, it’s important to design the knowledge agent and its supporting data infrastructure together. The rise of GeoAI and the use of vector databases for geospatial data show a trend toward deeper integration of spatial awareness into AI and data systems, enabling future agents to perform more advanced spatial reasoning beyond just tracking coordinates.

3.3 Fusing Diverse, Dynamic Data Sources

A key role of knowledge agents is to combine information from various sources to create a clear operational picture or actionable plan. It's important to understand the difference between data fusion and data synthesis here. Data fusion involves combining multiple measurements of the same phenomenon, often from different sensors, to get a more accurate estimate. For example, this might mean merging position data from AIS, radar, and GPS to get a better vessel track. Data synthesis, on the other hand, is a more complex process. It integrates information from different types of sources, like weather forecasts, vessel traffic data, cargo manifests, regulations, and real-time vessel performance data, to build a full understanding of the situation and what it means.

Creating effective and reliable synthesis in the maritime field is particularly tough due to several challenges:

• Heterogeneity: Data comes in many different formats and structures, such as structured numerical data (like sensor readings), unstructured text (like reports and regulations), semi-structured data (like logs and manifests), and complex geospatial and time-series data. Integrating these requires advanced techniques to transform and align them.
• Dynamism and Velocity: The maritime environment is always changing. Real-time updates from AIS, weather services, and onboard sensors come in continuously, so the synthesized operational picture must be updated and reassessed regularly.
• Veracity and Conflict: Maritime data sources can be unreliable. AIS data may have issues like noise, incompleteness, or manipulation (like spoofing or jamming). Sensor readings might be inaccurate due to calibration problems or failures, and different sources may provide conflicting information, such as differing weather forecasts. Synthesis needs to include ways to handle this uncertainty and resolve conflicts.
• Contextualization: Raw data points are often meaningless without context. The synthesis process must interpret data within the larger operational context, for instance, figuring out if a vessel's slow speed near a port is normal anchoring behavior or a potential issue needing further investigation.
• Scalability: Synthesizing complex data across thousands of vessels globally, each producing multiple data streams, demands highly scalable computing systems and efficient algorithms.

To meet these challenges, various technologies are being used. Semantic enrichment techniques, often involving ontologies and knowledge graphs, help align different data types and show relationships clearly. Multi-modal AI models are being developed to process different types of information (like text, images, and sensor data) simultaneously. Advanced data fusion platforms, such as those made by Windward or CLS, use advanced algorithms and AI to clean, validate, cross-reference, and integrate data from many sources (like terrestrial AIS, satellite AIS, shipborne AIS, RF detection, and vessel databases) to tackle issues of noise and manipulation. Ultimately, the reasoning ability of the AI agent plays a crucial role in interpreting the combined data and drawing relevant conclusions.

Despite advancements, achieving reliable real-time synthesis across all relevant maritime data sources is still a significant research and engineering challenge. The success of knowledge agents depends on overcoming this hurdle, as the quality of their reasoning, planning, and decision support relies on the accuracy and completeness of the information they work with. Given the reliability issues in maritime data, synthesis cannot just be about aggregation; it must involve critical evaluation, cross-validation between sources, and anomaly detection to identify and manage unreliable or manipulated data before it skews the agent’s understanding of the situation.

3.4 Data Quality and Standardization Imperatives

The principle of "garbage in, garbage out" is very important for AI systems, especially for knowledge agents that use complex reasoning. The quality of an agent's outputs depends on the quality of the data it uses. If the data is bad, the analyses will be flawed, predictions inaccurate, recommendations unsafe, and users will lose trust.

In the maritime sector, there are major challenges with data quality from different sources:

• AIS Data: This data often has gaps in coverage, especially from satellites in busy areas. It can be inaccurate, delayed, inconsistently reported, and can be manipulated to hide illegal activities or create navigational risks.
• Vessel Sensor Data: This data can have problems like sensor drift, requiring frequent calibration, sensor failures, signal noise, and a lack of standard formats and communication methods among various manufacturers and vessel types.
• Manual Inputs and Logs: Data entered by hand, such as logbooks and maintenance records, can be inconsistent, incomplete, and filled with human errors.
• External Data Sources: Information from third parties, like weather services or port authorities, might come in different formats, have different resolutions and update frequencies, and may be hard to access or costly.

To fix these issues, we need to focus on data standardization. Creating and adopting industry-wide standards for data formats, definitions, and Application Programming Interfaces (APIs) is essential. Standardization helps with data sharing between systems (like ship-to-shore and ship-to-ship), lowers the cost and complexity of developing AI solutions, and enables the creation of compatible tools and applications.

Organizations like the IMO and the Digital Container Shipping Association (DCSA) help promote these standards.

In addition to standardization, good data governance and quality management practices are critical. This includes strict data cleaning and validation, regular sensor calibration, investment in high-quality sensors, tracking data lineage, and ensuring data integrity throughout its life cycle.

The lack of standardization is a major barrier to economic growth and innovation in maritime AI. It raises the cost and time needed to create AI applications, as developers must spend a lot of effort on custom data cleaning and integration for each project. This slows innovation and limits applications that depend on data exchange among stakeholders, like optimizing port calls with shared ETA data.

Moreover, data quality is not just a technical issue; it directly affects trust and safety. If knowledge agents use unreliable or manipulated data, their outputs cannot be trusted for important decisions, especially those that involve safety. This can lead to dangerous situations. Therefore, ensuring high data quality and promoting standardization are necessary for building trustworthy, safe, and effective knowledge agents in the maritime industry.

4. Ensuring Trust and Effective Human Oversight

Successful use of knowledge agents in maritime operations depends on building trust between human operators and the AI system. This requires clear communication and strong oversight. Because maritime activities are high-stakes, especially regarding safety and regulatory rules, simply showing that the agents perform well is not enough. Users need to understand how the agents make decisions and recommendations, so they feel confident in them.

4.1 Explainable AI (XAI)

Many advanced AI models, often called "black boxes," are not suitable for safety-critical maritime applications. In these contexts, understanding the reasoning behind a decision is crucial for safety, verification, and accountability. Explainable AI (XAI) includes methods that make AI decision-making processes clear and understandable to users.

Using XAI in maritime operations provides several important benefits:

• Building Trust: Operators, like captains and shore managers, need to understand how decisions are made to trust the AI’s outputs.
• Verification and Debugging: Explanations help users and developers confirm that the AI is acting correctly, find any flaws or biases, and fix unexpected issues.
• Regulatory Compliance and Auditing: Regulators now require transparency. XAI enables audits of AI decisions to ensure compliance with safety regulations.
• Enhanced Human-AI Collaboration: When users understand the AI’s reasoning, they can work together more effectively, knowing when to trust the AI.
• Bias Detection: XAI can highlight unintended biases in data or models that may lead to unfair or unsafe results.
• Accountability: Clear explanations allow users to trace decisions back to specific inputs and reasoning steps.

Different XAI techniques offer varied approaches to explaining decisions:

• Model-Specific vs. Model-Agnostic: Some techniques are made for specific models, while others can be used with any black-box model by analyzing its input-output behavior.
• Local vs. Global Explanations: Local explanations focus on a single decision, whereas global explanations describe the model's overall behavior.

Common XAI methods include:

• LIME (Local Interpretable Model-agnostic Explanations), which approximates the black-box model with an easier-to-understand one.
• SHAP (SHapley Additive exPlanations), which uses game theory to show how much each input feature contributes to the output.
• Extracting simple rules from complex models, ranking input features by importance, generating counterfactual explanations (like, "What needs to change for this alert not to trigger?"), and visualizing how models make decisions, such as showing attention weights in transformer models.
• Linking explanations to data provenance—understanding where the data comes from—is crucial for traceability.

Applying XAI in maritime settings comes with its own challenges. Explaining decisions based on diverse and sometimes noisy data can be tough. Offering real-time explanations in a way that operators can easily understand during stressful situations is challenging but necessary. Explanations must also be suited to different user roles; for instance, a captain needs immediate operational context, while a data scientist requires detailed model parameters. It's essential to communicate uncertainties tied to predictions or recommendations clearly.

Current research, like the University of Delaware’s work on XAI for seafloor mapping and the UK's SeXTANt project on trustworthy AI navigation, addresses these challenges.

Another key point is that maritime XAI must explain not just the final AI calculation, but also the steps leading up to the decision. Since decisions depend heavily on data synthesis, explanations should clarify how raw data was combined and interpreted. Simply listing important features of the model is not enough if those features are complex. Effective XAI in this field requires methods that can trace decisions back through all reasoning layers to the original data sources, emphasizing the need for robust tracking of data origins.

This requirement for deep explanations will likely affect how maritime knowledge agents are designed. Systems that prioritize modularity and clarity, possibly preferring hybrid designs or more transparent models, such as rule-based systems or decision trees, may be favored over complex black-box systems, particularly for safety-critical functions.

Table 2: Applicable XAI Techniques for Maritime Knowledge Agents

4.2 Human-AI Collaboration Models

Knowledge agents will work with different levels of human oversight, ranging from close supervision to more independence, depending on the task, situation, and the system's maturity. Similar to how Maritime Autonomous Surface Ships (MASS) are categorized by levels of autonomy, knowledge agents can operate within various collaborative models:

• Human-in-the-Loop (HITL): In this model, the AI acts mainly as a decision-support tool. It analyzes data, provides insights, predicts outcomes, and suggests actions, but the human operator has the final say. This setup is crucial for complex or uncertain situations where human judgment is essential. Examples include confirming risky maneuvers suggested by the AI, changing a route due to unexpected local conditions (like debris or urgent requests), or interpreting complicated regulations.
• Human-on-the-Loop (HOTL) / Supervisory Control: Here, the AI handles tasks on its own based on its programming and goals, while the human operator keeps an eye on its performance and steps in only if needed. This model suits routine or repetitive tasks where the agent has proven reliable but still requires oversight for safety or quality. Examples include making routine log entries, monitoring vessel systems for specific problems, executing approved voyage plans in open waters, or conducting straightforward compliance checks.

The key principle for using these models in maritime work should be “Trust, but Verify.” Operators need to believe in the agent's skills but must always have the ability to check its outputs and actions, especially in critical situations. This requires systems designed for easy verification. Agents should explain their recommendations clearly and allow operators to ask questions, review the data the agent used, understand its confidence levels, and have straightforward ways to override or adjust the agent’s actions if necessary. Trust must be earned through consistent, reliable performance, transparency, and enough control for the human operator.

It's important to understand that knowledge agents are meant to enhance, not replace, human expertise. Humans provide crucial deep knowledge, deal with unique or unexpected situations that the agent isn’t trained for, make subjective decisions about risks or priorities, maintain safety standards, and handle complex ethical issues. This teamwork demands that maritime professionals improve their skills in data interpretation, AI interaction, and effectively overseeing and understanding AI outputs.

The best human-AI collaboration model will likely change over time. It should be flexible and based on the situation, adjusting the level of human oversight according to factors like the task's importance, the environment's complexity (like busy ports compared to open ocean), the agent's confidence in its analysis, and the possible consequences of mistakes. Systems should support smooth transitions between HITL and HOTL modes, possibly prompting human confirmation for high-risk choices or when conditions stray from normal. Additionally, applying “Trust, but Verify” effectively isn’t just about AI; it relies heavily on well-designed user interfaces. If the interface makes it hard for operators to access explanations, check data, or offer guidance when under pressure, they might skip the verification step, leading to blind trust or unsafe results. So, clear and efficient design is necessary to make the “Trust, but Verify” principle work effectively.

4.3 Challenges in Human Oversight for Autonomous Systems (MASS Lessons)

The development and regulation of Maritime Autonomous Surface Ships (MASS) show us the challenges of having human oversight in highly automated systems, which also applies to advanced knowledge agents. The International Maritime Organization (IMO) is working on assessing how existing maritime laws relate to MASS operating at various levels of autonomy.

Key challenges regarding human oversight in MASS include:

• Defining Roles, Responsibilities, and Liability: As machines become more autonomous, it’s crucial to clarify who is responsible for what. This includes whether the remote operator, vessel owner, manufacturer, or software developer is liable if something goes wrong.
• Maintaining Situational Awareness: We need to ensure that remote operators or a smaller crew onboard can stay aware of their surroundings, even without the full sensory input available on traditional ships. There are risks of operators becoming complacent, overwhelmed with information, or losing essential seamanship skills due to relying too much on automation.
• Human-Machine Teaming and Interaction: Effective communication between human operators and autonomous systems is essential. This is especially important in areas where traditional vessels and MASS must work together or during times when control shifts from humans to machines.
• Trust, Reliability, and Assurance: For people to accept highly automated systems, they must trust in their safety and reliability. This requires thorough testing, validation, and new assurance processes to manage the complexity of AI systems.
• Cybersecurity: Automated and connected systems can be targets for cyber-attacks. It's vital to secure communication links, control systems, and data streams from threats like jamming, spoofing, or unauthorized access.

These challenges also relate to knowledge agents. Even if an agent isn’t controlling the vessel, its role in providing important information and support for decision-making requires careful thought about trust, situational awareness, role definitions, human-agent interaction, and system security.

MASS development is pushing the maritime industry and regulators to confront these key issues about human roles, the limits of automation, liability in automated systems, and trustworthy AI. Guidelines and best practices from MASS initiatives will likely shape the requirements for using advanced knowledge agents. Experience with automation shows that the “human element” challenge changes rather than disappears. We need to focus not only on manual errors but also on possible failures in system design, operator training, interface design, and human-machine coordination. Addressing these can improve safety through strong design methods, thorough training, clear operational procedures for AI interaction, and a strong safety culture that encourages scrutiny of automated systems.

5. Designing Effective Knowledge Agent Interfaces

The user interface (UI) is a key link between complex technology and the people using it. Good UI design is essential for building trust, allowing users to verify information, and promoting efficient use of advanced AI systems. The design should do more than just show data; it should help users understand and act on the insights and reasoning of the agent.

5.1 Product Affordances

An important requirement for trust and verification in high-stakes maritime situations is that the user interface (UI) must help users understand how the agent arrives at its decisions. This means the interface needs to be transparent.

Key design features to achieve this include:

• Clickable Data Sources / Traceability: Users should easily trace the agent's output, like risk warnings or route suggestions, back to the data that influenced it. This could involve clickable links or icons that show the source data, such as specific weather forecasts, regulatory text, tracking segments, or sensor readings that triggered an alert.
• Step-by-Step Reasoning Visualization: For complex tasks, like optimizing a voyage, showing the agent's reasoning can help users understand. This can take the form of a simple flowchart or checklist that mimics how humans make decisions, illustrating how the agent reached its conclusion.
• Explanation Prompts: Including clear prompts like "Explain this finding," "Show evidence," or "Why was this risk flagged?" allows users to ask for clarification when needed.
• Confidence Scores: Displaying the agent's confidence level (as a percentage or a simple rating) alongside predictions gives users important information about how reliable that information is.
• Highlighting Key Factors: Visually stressing the most important data points in a decision, like a critical wind speed in a weather warning, helps users quickly understand why the agent made a particular output.

Creating effective explanations is challenging. They must provide enough detail for users to verify information while remaining concise enough to be useful in high-pressure situations. If explanations are too complex or long, they can overwhelm users. Therefore, designing these features requires understanding how maritime professionals think and make decisions. The UI should fit with their existing processes, like presenting explanations during standard pre-voyage checks or navigation tasks. The ability to trace information back to its source (called provenance) is crucial not only for building trust but also for accountability, auditing, and troubleshooting issues in the agent's data processing or reasoning.

5.2 UI/UX Design Patterns for Complex Maritime Data Visualization

Visualizing the outputs of knowledge agents, which provide insights from various types of data, can be a significant challenge for user interface and user experience design. The goal is to present complex information clearly and simply. This clarity helps users understand it quickly and make better decisions, while also avoiding information overload that can occur with cluttered displays.

Effective visualization techniques include:

• Interactive Maps: These are often central for maritime operations. Maps should allow users to add different layers of data, such as optimized routes, vessel tracks, real-time traffic, weather patterns, risk zones, environmentally sensitive areas, security alerts, and port layouts. These maps must be highly interactive, allowing smooth zooming and panning, filtering of layers, and direct querying of map objects for more details.
• Dashboards: Configurable dashboards are vital for showing key performance indicators (KPIs), active alerts, system health, and overall operational context at a glance. Given the amount of information available, dashboards should focus on clear visual hierarchy, appropriate information density, and customization for different roles and tasks.
• Timelines: Timelines are essential for showing information over time. They can display past events (like vessel tracking history), current status, and future predictions made by the agent, such as estimated times of arrival (ETAs), predicted weather windows, and scheduled maintenance.
• Data Tables and Lists: These are necessary for organizing structured information like vessel details, cargo information, compliance checklists, and active alerts. Good design should include sorting, filtering, and search features to help users quickly find what they need.
• Contextual Displays: New methods are emerging that integrate information directly into the operator's view. This might include overlaying alerts or target information onto live camera feeds or embedding insights within common interfaces like ECDIS displays.

Certain UI patterns from broader design can also be adapted, such as Progressive Disclosure (showing details as needed to avoid clutter), a clear visual hierarchy that emphasizes primary actions, and Breadcrumbs for navigating complex information. AI can also help with features like auto-fill and suggestions. Resources like UI kits and design systems can provide essential components and ensure consistency.

A key principle for visualizing knowledge agent outputs is to focus on synthesis and context. The UI should visually represent the relationships and insights derived from data, rather than just display raw data. For example, instead of showing a weather map and a vessel track separately, the UI might show the planned route overlaid with color-coding for predicted risk levels.

Personalization is also important. Since different users (like captains, engineers, operations managers, and logistics coordinators) have different needs, an interface that adapts the displayed information based on user roles and tasks can significantly improve usability and reduce cognitive strain.

5.3 Supporting Human Interaction and Guidance ("Nudging the Model")

Effective collaboration between humans and AI needs user-friendly interfaces that allow people to actively engage with and guide the AI. This goes beyond just receiving information passively. The system should let users adjust the AI's settings based on their own knowledge and context.

Key ways for users to interact include:

• Parameter Adjustment: Users should be able to change important settings that affect the AI’s decisions. For instance, they can choose whether to prioritize fuel efficiency or sticking to a schedule in voyage planning, set tolerance levels for alerts, or define specific operational limits like minimum clearance.
• Goal Refinement: There must be options for users to clarify or change the AI's main goals for specific tasks. For example, they might tell the AI to prioritize safety during bad weather or focus on analyzing a potential hazard.
• Feedback Provision: The system should have simple ways for users to give feedback on the AI's suggestions. This could include rating suggestions, correcting mistakes, or explaining why they disagreed with a recommendation. This feedback is essential for both immediate corrections and the AI’s long-term learning.
• Direct Manipulation: Users should be able to directly change the AI’s outputs when needed. This might include adjusting an automatically created route on a map or changing a log entry.
• Querying and Dialogue: The system should support users asking questions in everyday language or through structured prompts. Users can ask for alternative routes with less risk, seek more details about a prediction, or examine different scenarios.

These interaction options reflect best practices for designing Decision Support Systems (DSS). They focus on presenting choices, justifying those choices, and allowing users to make and control selections.

Providing these options is crucial for managing the uncertainty and ambiguity of real-world maritime operations. AI trained on historical data may struggle with new situations or factors not included in its training. Human operators bring valuable intuition and context that help them navigate such uncertainty. Allowing users to guide the AI helps bridge the gaps in the AI's capabilities, resulting in better and more relevant outcomes.

Moreover, how these interaction methods are designed affects user trust and acceptance. When users feel they can control the system, give input, and guide the AI, they are more likely to trust and use it. If the system is rigid and offers no way to correct or guide, experienced professionals may resist and distrust it.

6. Conclusion and Strategic Recommendations

6.1 Knowledge Agents as a Force Multiplier in Maritime Operations

Knowledge agents are a major step forward from traditional automation and analytics in the maritime industry. They act as smart partners that can see their surroundings, think, plan, and take action. They do this by combining large amounts of complex data from sources like AIS, sensors, weather information, regulations, and operational logs. This ability helps tackle the problem of information overload that maritime professionals face.

By turning raw data into useful insights, knowledge agents can greatly improve operational efficiency through better planning and logistics. They enhance safety by identifying risks early and supporting decision-making. They also provide deeper insights by analyzing complex data patterns and help ensure compliance with regulations. Knowledge agents can boost human capabilities, allowing for data-driven decisions on a scale that was not possible before.

6.2 Data, Technology, Trust, and Integration

Using maritime knowledge agents has great potential, but there are several challenges to overcome for them to be used effectively. The main issue is data management. We need to handle the large amounts of data quickly, while also addressing problems like poor quality, inconsistencies, lack of standardization, and the risk of manipulation in many maritime data streams.

Another challenge is the technical complexity of combining different data sources reliably and in real-time. We must build and maintain trust by addressing the "black box" issue through clear explanations of AI decisions. This transparency helps operators understand and verify the reasoning behind safety-critical choices.

It is important to define how humans and AI can work together and create clear guidelines for human oversight, following the principle of "Trust, but Verify." We also need to ensure that these systems can work with older technology and existing processes, which presents both logistical and technical challenges.

Robust cybersecurity is essential to protect these interconnected and data-dependent systems from disruptions or threats. Finally, we must close the skills gap by training maritime professionals to work effectively alongside AI for successful adoption.

6.3 Recommendations

To effectively use knowledge agents in the maritime industry, all stakeholders—shipping companies, port authorities, technology providers, regulators, and training institutions—need a proactive and strategic approach. Here are some key recommendations:

• Improve Data Governance and Infrastructure: Focus on projects that enhance data quality across the industry. Join efforts to adopt common data standards to improve compatibility. Upgrade data systems to manage the large and complex data needed for AI, including investments in cloud services, real-time data processing, geospatial databases, and possibly knowledge graph technologies.
• Require Explainable AI (XAI): Ensure that AI systems are transparent and easy to understand, especially those that impact safety and compliance. Invest in developing XAI techniques tailored to the maritime sector’s specific data and operational needs.
• Use Human-Centric AI Design: Create knowledge agents as tools that support human decision-making, not just replace it. Pay attention to user-friendly designs that make it easy to understand and interact with the systems. Set up mechanisms for human oversight and intervention, embodying the principle of "Trust, but Verify."
• Prepare the Workforce for Change: Recognize that AI will change job roles. Invest in training programs to help maritime professionals develop skills in data use, AI interaction, system monitoring, and making decisions about automated systems. Address concerns about changes in job roles.
• Encourage Collaboration and Pilot Projects: Foster partnerships between technology creators, users (like shipping lines and ports), and classification societies to carry out real-world pilot projects. These projects are essential for testing and improving knowledge agent solutions, sharing best practices, and building trust in the technology.
• Engage with Regulatory and Ethical Frameworks: Work closely with regulators, such as the IMO and national authorities, to shape clear and practical guidelines for the ethical and safe use of AI and autonomous technologies in maritime operations. Discuss key issues like liability, certification, and data privacy.

6.4 What’s next?

The development of knowledge agents in the maritime sector is a continuing process. We expect ongoing improvements in AI reasoning, learning, and synthesis abilities, which will create agents that are more advanced and capable. Their use alongside other new technologies offers great potential. Knowledge agents could become central to decision-making for Maritime Autonomous Surface Ships (MASS), allowing these ships to perform more complex tasks.

By connecting with Maritime Digital Twins, virtual models of ships, ports, or systems, agents could test scenarios, enhance performance, and manage operations with high accuracy.

Most importantly, knowledge agents could help integrate the maritime ecosystem more deeply. They can enable smooth, intelligent data sharing and coordination among different entities, like ships, ports, cargo owners, logistics providers, and regulators. This integration could lead to greater efficiency and resilience throughout the global maritime supply chain.

In summary, knowledge agents are not just a futuristic idea; they are becoming a real technology that could change the maritime industry. By effectively handling the complexity of modern maritime data, they can pave the way for safer, more efficient, and more sustainable operations. To achieve this potential, the industry must collectively address challenges related to data, technology, trust, human factors, and regulation. This journey needs investment, collaboration, and a focus on human-centric design, but the benefits of a truly intelligent maritime ecosystem are significant.

Disclaimer & Licensing

This research is provided for informational purposes only.
I have made efforts to ensure the accuracy of the information but do not guarantee its completeness or applicability. The grammar and spelling of this text have been reviewed using automated tools like Grammarly; however, some errors or nuances may remain. The views expressed are personal opinions and subject to change, especially given the rapidly developing nature of Artificial Intelligence in the maritime field. This content should not be considered professional advice, and readers rely on it at their own risk.

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