United States

Individual warfighter readiness is critical to the success of the Navy mission; however, prediction of readiness status using standardized parameters in Navy warfighters is currently lacking. Data-driven machine learning applications have innovated prognostic algorithms for complex human diseases, traits, and other biological states. Despite these promising advances, relatively small sample sizes and a limited number of features per sample can pose challenges. This study addresses the knowledge gap of readiness prediction modeling using variables such self-reported physical health (e.g., pain, fatigue) and mental health (e.g., anxiety) symptoms, and selected gene variants related to the human stress response. Readiness was measured using the validated 15-item Military Readiness Scale (MRS-15). In a sample of n = 194, individuals were dichotomized into “high” (n = 90) and “low” (n = 104) readiness status using a cutoff of 77 out of 100 in the MRS-15 (equivalent to a readiness level of 77%). Initial results from the stepwise regression narrowed the predictive variables of military readiness to self-reported variables such as pain, fatigue, and anxiety, and two gene variants: one variant of the catecholamine enzyme, catechol-O-methyltransferase (rs737865), and one variant of the dopamine D2 receptor (rs1800497). Due to the limited data availability in this study, we implemented two models using random forest and LASSO-based classifiers to predict the total military readiness of a particular warfighter. A comparative analysis was run using random forest and LASSO to evaluate robustness in predicting high and low readiness status. In early comparisons of the models, preliminary results indicated that the random forest approach performed marginally better than LASSO in predicting readiness. We will present a complete summary of our results at the workshop. Overall, using machine learning techniques to develop predictions from warfighter data can lead to novel fleet readiness management tools that inform, and expedite, Navy leadership decisions.

NAML 2025

Predicting Military Readiness In Naval Warfighters

readiness

human factors

prediction

poster

### Welcome to the 9th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. 
 Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications
* To connect machine learning researchers with experts in military requirements
* To build and strengthen collaborations within the defense research community

#### *"What we do as a Navy is a human business. Don’t pass up an opportunity to come together as a team. Don’t pass up leadership opportunities, and don’t outsource them to a PowerPoint. If you have an opportunity to come together as a team, to increase belonging, do it."*
Admiral John M. Richardson, 31st Chief of Naval Operations

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The 9th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.

This work addressed ways of making machine learning more robust against  adversaries manipulating its input data, as with input sensor data an adversary can at least partially control. The approach we explored was training and comparing alternative artificial-intelligence models for the same data. Our project tested this solution on real ship-tracking data from the public AIS (Automated Identification System) database available from the U.S. Coast Guard for U.S. coastal waters. We built ship tracks out of individual ship records and identified ten key features of tracks that help classify ship types, with the goal of identifying ships falsely reporting their identities. This identified some interesting errors and suspicious anomalies in the data such as impossible jumps in ship positions. We then systematically perturbed the data to various degrees to see how that affected the ship-type classifications with eight standard machine-learning methods. We were particularly interested in nonlinear effects that did not uniformly increase with the degree of perturbation, effects that varied significantly with the direction of perturbation, and in major differences in effects between the machine-learning methods. Our results did find some interesting weaknesses in the methods, and our methodology is general enough to be applied to other kinds of machine-learning tasks.

Adversary Attacks on Ship Tracking

Leadership coaching is used to support the transfer of leadership skills from the classroom to operational environments. However, it traditionally involves one-one-one development by professionally trained coaches, which requires a significant investment of resources for both training and logistics support. In this ONR-funded project, an interdisciplinary team developed a prototype AI-based leadership coach for planned use at the United States Naval Academy (USNA). The platform provides a series of individualized coaching sessions aligned with professional coaching standards, along with integrated crisis detection and user goal management, making the benefits of leadership coaching more broadly available at a fraction of the cost of a traditional coaching intervention.

Two versions of the platform are being developed concurrently, pending access to users, for both USNA midshipmen and leaders of student groups at the University of Minnesota (UMN). Both face similar challenges in identifying how to apply leadership concepts to real-world challenges, and the coach, named Nereus at USNA and Olivia at UMN, holds open-ended conversations with users helping them to understand and plan for everyday challenges. Over the course of multiple sessions, the coach works to develop actionable goals and then holds the coachee accountable in the following week. This also blurs the line between traditional and agentic AI in that the coach AI must drive the interaction and control the flow of individual sessions to meet its own goals invisibly to the coachee.

To successfully develop this prototype, core expertise from industrial-organizational psychologists was required as the traditionally favored approach to AI development by computer scientists, the collection of training data, was not feasible. Specifically, there exists no full-text database of successful coaching interactions with midshipmen to serve as exemplars for successful coaching to serve as a reinforcement data to train a custom large language model. Instead, the coach must be developed from guidance by subject matter experts in the coaching domain. This necessitates an explicitly interdisciplinary approach. Additionally, such a team is better capable of planning for and building detection models to meet secondary goals, like the automated detection of risk for imminent self-harm, and to develop appropriate procedures for the response to such events.


Developing an AI-based Leadership Coach for the US Naval Academy

A Network Science and System Dynamics Simulation Framework for Installation Resilience under Compound Extremes

For weather-informed ship route planning, optimizing for fuel efficiency entails predicting how the ship will react to the ocean environment during a voyage. Fuel consumption over a route is strongly dependent on ship resistance, which is primarily due to calm water resistance and added resistance in waves. Physics-based models of ship hydrodynamics can be employed to compute resistance as function of the hull form geometry, and ocean wave conditions. However, development of these models entails a significant amount of detailed information about the ship geometry, dimensions, and loading conditions, which are often not readily available. Additionally, there is a significant computational cost associated with the development of physics-based models. To address these challenges, this study investigates the feasibility of robust data generation for model training as part of the Machine Learning Surface ship Resistance estimation Framework (ML-SURF). This framework is focused on developing AI-enabled models to rapidly estimate calm water resistance and added resistance in waves based on a limited set of ship information.

The first step in this framework entails rapid generation of a wide range of ship geometries resulting in approximately 2,000 hull forms. Hull form geometries are generated using Rhinoceros 3D (Rhino), which is an advanced computer-aided design and graphics tool. In Rhino, this entails manipulation of the control points of a baseline parent design to generate new ship geometries based on a set of hull shaping parameters. Once the ship geometries are generated, calm water resistance and added wave resistance is computed for each respective hull form as function of large set of ocean conditions (wave height, modal period, relative wave heading), and ship speeds. Resistance predictions are generated using fast, reduced-order physics-based modeling and simulations. The resultant dataset covers a significant variety of displacement mono-hulls for a wide range of ship shaping and sizing parameters, which is a key enabler for robust model development via machine learning. As part of ML-SURF, a companion study is focusing on exploratory analysis and model training with this dat

Ship Resistance Estimation Framework with Machine Learning: Data Generation

“Harnessing Spatial AI for Advanced Geospatial Intelligence”
The rapid evolution of artificial intelligence and geospatial technology has paved the way for unprecedented advancements in various sectors, and in an era marked by complex geopolitical dynamics and continuous innovation, the U.S. Navy's strategic operations increasingly rely on advanced data analysis and intelligence capabilities. The introduction of spatial artificial intelligent within the machine learning ecosystem offers a transformative approach to processing and interpreting geospatial information. Oracle will present how spatial artificial intelligent empowers the U.S. Navy to enhance its intelligence, surveillance, and reconnaissance operations, thereby improving strategic decision-making and operational effectiveness.
Spatial AI equips the U.S. Navy with sophisticated geospatial machine learning algorithms that are designed for the detection of spatial patterns, prediction of outcomes, and classification of spatial data. By leveraging these advanced capabilities, naval personnel can efficiently and more accurately analyze vast amounts of geospatial data, which is essential for identifying potential threats, monitoring maritime activities, and supporting national security. The comprehensive suite of algorithms provided by spatial AI is designed to analyze vector data and location-based events, facilitating actionable insights. The end-to-end workflow capabilities offered by Spatial AI, encompassing data preparation, model training, and application, enabling the U.S. Navy to fully harness the power of spatial data and enhance their operational effectiveness. 
Spatial AI can be seamlessly integrated with various existing systems and processes, offering the potential to enhance the U.S. Navy’s strategic operations. For instance, by leveraging advanced spatial AI technology’s comprehensive workflow capabilities, which include training spatial machine learning models, and applying these models to various applications, the U.S. Navy can enhance the integration of AI into the existing systems. This integration allows for the consolidation of diverse geospatial data sources, enabling the U.S. Navy to uncover hidden trends and patterns that might otherwise go unnoticed. 
In practical terms, spatial AI can enhance the U.S. Navy's ability to conduct maritime domain awareness operations by providing precise and timely geospatial intelligence. This capability can be applied to monitoring illegal activities, such as smuggling and unauthorized fishing, as well as for planning and executing naval operations with greater accuracy. Additionally, spatial AI supports the U.S. Navy's efforts in disaster response and humanitarian assistance by enabling rapid assessment and analysis of affected areas.
In summary, the implementation of spatial AI within the U.S. Navy represents a significant advancement in geospatial intelligence and strategic planning. By harnessing the power of spatial AI, the U.S. Navy can improve its situational awareness, enhance international cooperation, and make well-informed decisions in an increasingly complex and interconnected world.

Harnessing Spatial AI for Advanced Geospatial Intelligence

As with any tool, there are risks to misusing Large Language Models (LLMs). However, unlike with standard tools, even correct use of LLMs can cause harm – consider hallucinations: unfounded replies that are simply made up. 

Technological changes, such as upgrading a model to a newer, more performant version are relatively easy. Changing human behavior – how people use these tools – is much harder: established usage patterns can be hard to budge. 

To address this issue, our team created an LLM demo website for a client founded on human design principles to prioritize responsible usage. We emphasize the following features:
• Provide guidance on how models can be biased and can hallucinate 
• Provide information on which LLM was chosen and why
• Explain how the model was trained
• Reveal what information is saved, and what is not
• Calculate and provide automated diagnostics of model performance in real-time 
• Empower users to upload their own documents to see how well the model works

We believe this is a differentiated approach that focuses on the less malleable, but more important aspect of LLM interaction: the user. We believe this approach could and should be more widely used throughout the U.S. Government, and particularly within the Department of Defense (DoD). 


Human Centric Design to Foster Responsible LLM Use

The objective of this research was to evaluate machine learning-based optical character recognition (OCR) to automate recognition of aircraft side numbers on the carrier flight deck. In this research, we developed a software pipeline that utilizes a pan-tilt-zoom (PTZ) camera to achieve this automation. The pipeline detects all aircraft in view, a PTZ camera to obtain high resolution images of each aircraft, then OCR to extract the side number text. The final algorithm is demonstrated using a combination of stationary aircraft.


Automated Aircraft Sidenumber Recognition using Computer Vision for Naval Aircraft Logistics

Spiking Neural Networks (SNNs) [1], which are inspired by the functioning of the brain, offer a promising approach to artificial intelligence with significant advantages in energy efficiency. This makes SNNs particularly suitable for use in underwater edge devices, such as unmanned underwater vehicles (UUVs), which are often constrained by limited battery capacity. However, current SNN-based methods for underwater acoustic data representation learning remain relatively underexplored. In this work, we propose to investigate the use of Spike Timing Dependent Plasticity (STDP) [2] within SNNs for effectively analyzing underwater acoustic data. Our objective is to develop a representation learning method that requires minimal training data and enables energy-efficient data processing on UUVs in challenging underwater environments.

Underwater Acoustic Data Representation Learning with Spike Timing Dependent Plasticity


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The convergence of technologies—from foundation AI models to diverse sensors and actuators to ubiquitous connectivity—is transforming the nature of interactions in the physical and digital world. People have accelerated their collaborative connections and productivity through digital and immersive technologies, no longer limited by geography or language or access. Humans have also leveraged and interacted with AI in many different forms, with the advent of hyperscale AI models (i.e., large language models) forever changing (and at an ever-astonishing pace) the nature of human-AI teams, realized in this era of the AI “copilot.” Similarly, robotics and automation technologies now afford greater opportunities to work with and/or near humans, allowing for increasingly collaborative physical robots to dramatically impact real-world activities. It is the compounding effect of enabling all three capabilities, each complementary to one another in valuable ways, and we envision the triad formed by human-robot-AI teams as revolutionizing the future of society, the economy, and of technology.

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Adversary Attacks on Ship Tracking

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