United States

The Marine Core Planning Process (MCPP) is a cyclical and disciplined process for military planners to develop, test, and ultimately transition Courses of Action (COAs) into orders. However, many steps within MCPP are manual, time intensive, and resource constrained. Under the COA Optimization, Analysis, and Comparison Harness (COACH) project, a research effort funded under the Office of Naval Research (ONR) Code 34 Science of AI Program, we aim to address these challenges of developing COAs by building Artificial Intelligence (AI) enabled decision-making tools and integrating them into MCPP. Specifically, we see two main challenges: 1) there are limitations in existing AI training frameworks that hinder the development and ultimate adoption of AI technologies for military decision-making processes, and 2) the data required to train such models is severely limited. To overcome these challenges, we will present Framework for Intelligent Simulation Command with Hierarchically Embedded Reinforcement Learning (FISCHER), a scheme for training AI decision-making models, and a newly developed, high-speed, low-fidelity military simulator to train COA generation models. FISCHER is an adapter between AI agent code and battlespace data streams, whether they be simulations of military operations or live real-world operations, to enable rapid prototyping of new AI models or to plug in existing models into new environments. We will present our current progress in training Reinforcement Learning (RL) agents, a type of Machine Learning (ML), in COA generation tasks. These developed tools under COACH will be integrated into the Higher Echelon Mission Planner (HEMP) and the Small Unit Mission Planner (SUMP), planning tools being developed under ONR Code 34’s Artificial Intelligence To Operations program.

NAML 2026

Course of Action Optimization, Analysis, and Comparison Harness (COACH)

course of action generation

marine core planning process

simulation

reinforcement learning

### 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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NAML 2025

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.

technical paper

### Welcome to the 10th 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.
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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 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



Analysis of civil considerations is the topic that may receive the least amount of time in the
planning course at the Expeditionary Warfare School (EWS) but is the step that can
potentially take the most time during operational planning. The Marine Corps Planning
Process (MCPP) calls for planners to evaluate the civil terrain by analyzing six
characteristics of the Area of Operations (AO): Areas, Structures, Capabilities,
Organizations, People, and Events (ASCOPE). For each of these ASCOPE categories, six
operational variables are to be considered: People, Military, Economic, Social,
Informational, and Infrastructure (PMESII).

Crosswalking the ASCOPE categories with the PMESII variables produces thirty-six
categories of civil considerations that each must be analyzed individually. The data that supports this analysis is typically complex, includes a large amount of unstructured text,
and is unique to the Area of Operations (AO). It is typically not possible to re-use previous
analyses or even data sources when performing MCPP in a new region.

Under sponsorship of ONR Code 34’s Artificial Intelligence To Operations program, the
Civil-Considerations thrust of our COA Optimization, Analysis, and Comparison Harness
(COACH) project uses the ASCOPE/PMESII conceptual structure to automate the process
of identifying, retrieving, and organizing civil considerations information.

COACH’s Civil Considerations Service (CCS) uses a Large Language Model (LLM) to identify
information relevant to each of the thirty-six crosswalk categories in a corpus of
unstructured text. Depending on the specific LLM used, the CCS can process non-English
data and produce English-language translations of the identified information. The CCS
produces a draft of the PMESII/ASCOPE crosswalk that users can then review and modify
to produce the final MCPP planning product.

The CCS is currently being integrated with the Higher Echelon Mission Planner (HEMP) and
the Small Unit Mission Planner (SUMP) tools, both of which are also AI2OPS projects.

Automation to Address Civil Considerations in the Marine Corps Planning Process

Extreme heat and cold stress pose serious risks to warfighter health, performance, and mission success. Individual susceptibility to thermal strain varies widely, so real-time physiological monitoring with intelligent analysis is critical to detect early signs of heat or cold injury [1]. We propose a two-stage wearable sensor-AI framework that integrates both mission planning and mission execution. In the planning stage, expected weather, operational activity, and warfighter demographics will serve as inputs to a Bayesian network-based causal inference model. This enables commanders to forecast likely physiological strain (e.g., elevated heart strain, skin temperature, hydration risk), simulate alternative scenarios, and adjust mission design (e.g., workloads, schedules, gear distribution) proactively before deployment. In the execution stage, we will employ continuous multimodal sensing (wrist, chest, helmet sensors) to capture real-time physiological and environmental data. Probabilistic AI models (Bayesian networks with temporal predictors) will infer key strain markers such as Relative Cardiac Cost, Skin Temperature, and Electrodermal Activity, and issue short-horizon forecasts (e.g., minutes ahead) to provide early warning of unsafe conditions. In prior work, we successfully deployed Empatica Embrace Plus wristbands and REED environmental sensors on 100 high-activity workers, building a Bayesian network regression model to predict skin temperature, cardiac cost, and electrodermal sweat signals from activity and weather data. This field study showed that low-cost wearables can yield actionable estimates of physiological heat stress under real-world conditions. Building on these results, our proposed approach will adapt the framework to military operations: integrating soldier-grade sensors (wrist, chest, helmet), tracking markers such as Relative Cardiac Cost (RCC), Skin Conductance Level/Response (SCL/SCR), skin temperature, and enhancing the AI model for real-time prediction and early warning. We will validate the system in the relevant field, simulate training exercises, and demonstrate integration with mission planning tools. This research aligns with ONR (Office of Naval Research) and NRL (U.S. Naval Research Laboratory) priorities in human performance and force health protection [2]. By enabling individualized monitoring and AI-based decision support, the proposed framework aims to improve warfighter survivability, reduce heat/cold casualties, and sustain mission readiness in extreme environments.

References
1. Josh Foster, Simon G. Hodder, Alex B. Lloyd, and George Havenith. 2020. Individual Responses to Heat Stress: Implications for Hyperthermia and Physical Work Capacity. Frontiers in Physiology Volume 11-2020, (2020). DOI: https://doi.org/10.3389/fphys.2020.541483. 
2. Office of Naval Research. n.d. Code 34: Warfighter Performance Department. https://www.onr.navy.mil/organization/departments/code-34.

Wearable Sensor Data-enabled AI Reasoning Framework for Warfighter’s Thermo-Physiological Survivability and Mission Readiness in Extreme Temperature

The development of robust vision-based navigation systems for maritime environments is critically dependent on the availability of high-quality, accurately labeled training data. Acquiring such datasets presents formidable logistical and technical challenges. A key difficulty lies in generating synchronized truth data, which requires capturing precise, time-aligned pose information, encompassing position and attitude, for both the UAV and the ship simultaneously. Without this critical truth data, collected imagery lacks the fidelity needed to train the advanced models essential for autonomous navigation. The Vision POD (VisPOD) system was purposefully designed to address these challenges and enable the creation of robust datasets for maritime machine learning applications.

As an advanced data collection system, VisPOD integrates with unmanned aerial vehicles (UAVs) to capture high-quality datasets for the development of vision-based navigation models. VisPOD captures multi-modal datasets across varied positions and environmental conditions, providing the comprehensive data needed for robust algorithm development. Additionally, its precise pose tracking and adaptable imaging capabilities ensure datasets are optimized to meet operational objectives. 

With its scalable architecture and ability to adapt to mission needs, VisPOD represents a powerful tool for advancing maritime data collection and driving innovation in machine learning applications. VisPOD is the essential data collection component of the Vision Navigation (VisNAV) pipeline, delivering datasets that power the automated labeling process, ultimately driving algorithm training and model development. This presentation will explore VisPOD’s capabilities and its role in delivering high-quality datasets for next-generation vision-based navigation machine learning models.


VisPOD’s Vision: Transforming Data Collection for Smarter Machine Learning Models

Cyber-physical systems (CPS)—composed of interdependent IT and OT components—underpin critical infrastructure, industrial control systems, and modern enterprises. Yet CPS security remains fragmented: traditional IT tools often disrupt fragile OT processes, while OT security practices rely heavily on isolation and obscurity. This gap leaves CPS vulnerable to increasingly sophisticated threats mapped across the MITRE ATT&CK framework and demonstrated in real-world incidents such as ransomware-induced disruption of industrial operations. To address this persistent risk, this dissertation proposes an Industry 4.0–aligned cybersecurity framework that integrates high-fidelity digital twins (DT), specialized artificial intelligence security agents, and continuous integration/continuous deployment (CI/CD) principles to evaluate, remediate, and strengthen CPS security without jeopardizing live operations.

The research constructs a cloud-based DT of an operational CPS modeled on the Alabama A&M University Industry 4.0 laboratory—a representative segment of the Tennessee Valley Authority’s critical infrastructure. This DT replicates network-store-compute elements, OT device behavior, and interdependencies at sufficient fidelity to support destructive testing, iterative remediation, and performance measurement. Within a CI/CD-like pipeline, multiple specialized AI agents independently perform vulnerability scanning, OT-safe threat hunting, configuration assessment, and policy verification. Their outputs feed into a human-in-the-loop (HITL) supervisory process supported by a general-purpose LLM acting as an analytical integrator. This structure enables the identification of false positives, confirmation of actionable threats, ranking of remediation priorities, and comprehensive change-control documentation aligned with NIST and OMB requirements.

A quasi-experimental design evaluates two hypotheses: whether modern enterprise EDR alone is sufficient to secure CPS, and whether multifactor, DT-based AI testing yields superior defensive posture. Statistical analysis across repeated, controlled trials demonstrates that EDR-only approaches miss classes of OT-specific vulnerabilities and occasionally introduce operational risk. In contrast, the multifactor DT pipeline provides earlier detection of compound vulnerabilities, safer remediation pathways, and improved assurance of CPS resilience, all without disrupting production systems.

This work shows that DT-driven, AI-augmented CI/CD pipelines can transform CPS cybersecurity from reactive patching to proactive, model-verified resilience. The proposed framework is generalizable across energy, transportation, healthcare, defense, and other high-risk sectors where CPS integrity is mission-critical. Future extensions include LLM-based autonomous orchestration, broader OT coverage, and integration of continuous mission-impact modeling.

Enhancing Cyber-Physical System and Continuos Devsecops Using Specialized and General Purpose AI Within Digital Twins

Two central challenges in multi-agent reinforcement learning (MARL) are (i) effectively aggregating information from local observations through inter-agent communication, and (ii) improving communication efficiency by reducing unnecessary message exchanges. The solutions for these challenges are inherently conflicting to each other. While information aggregation is crucial for distributed agents to build global awareness and make effective decisions, indiscriminate aggregation often leads to excessive communication overhead. In this talk, we present a novel centralized training with de-centralized execution (CTDE) framework, BElief-based Predictive Auxiliary Learning (BEPAL) with communication reduction, to address both challenges. Compared to traditional reinforcement learning, agents in BEPAL maintain a richer and more accurate latent representations of environment dynamics with the help of the auxiliary learning task, which reconstructs a belief of the global environment from local observations and inter-agent communications. In addition, it uniquely incorporates an unlearning-based communication advantage baseline, enabling agents to learn when to suppress uninformative messages during training, thus improving communication efficiency. We evaluated BEPAL on four cooperative MARL benchmarks: Predator-Prey, Traffic Junction, Google Research Football, and Robotic Warehouse. In all environments, BEPAL consistently outperforms prior methods, achieving an average 31.33% improvement in performance and demonstrating more stable convergence. The communication unlearning stage effectively reduces communication by 22.25% with nearly no impact to the agent’s performance.

Communication Efficient Multi-Agent Systems using BEliefbased Predictive Auxiliary Learning

Cognitive overload in naval personnel is a critical operational risk, yet forecasting it remains challenging. While existing deep learning models can predict performance from physiological data, their reliance on purely statistical mappings without biological constraints makes them brittle to sensor changes and provides limited insight into the underlying neural mechanisms of fatigue or stress.

Our work introduces a new paradigm by embedding a classic model from computational neuroscience, the Wilson-Cowan equations, within a deep learning architecture. This structurally informed approach allows us to learn a personalized, low-dimensional dynamical system for each individual, governed by the physics of neural excitation and inhibition.

Our framework comprises three novel machine learning stages. First, a sensor encoder, pre-trained with an anchor-view contrastive objective, fuses heterogeneous sensor data into a modality-agnostic latent space. Second, a deviation-based variational autoencoder extracts a static trait vector by modeling each individual as a unique delta from a learned population-level baseline. Finally, a hypernetwork maps this trait to the specific parameters of a personalized Wilson–Cowan stochastic differential equation, which models the evolution of the subject’s cognitive state as a continuous trajectory.

Validation on complex synthetic systems demonstrates the model’s ability to accurately recover the system’s underlying governing dynamics from observational data. We further show its effectiveness by training on high-fidelity laboratory data and applying it to diverse, high-demand tasks analogous to operational contexts, such as simulated driving and surveillance camera monitoring. The system learns to distinguish between stable focus states and unstable overload states.

This framework provides a direct measure of neural stability. For naval applications, it enables robust fatigue monitoring using available sensors and lays the groundwork for adaptive interfaces in command and control systems that can reconfigure displays in response to an operator’s measured cognitive state, mitigating overload before performance degrades.

Neuro-Dynamic Digital Twins: Biologically-Constrained Cognitive State Modeling from Physiological Signals

Background: Military operations are rapidly changing due to new robotic technologies. As evidenced in Ukraine, unmanned systems are becoming indispensable tools for situational awareness, strike, logistics, and force protection. Therefore, skillful deployment, operation, maintenance, and repair of unmanned systems are becoming essential for unit lethality and survivability. To address these needs, NAVADMIN 036/24 announced the establishment of the Robotics Warfare (RW) Specialist Rating. Currently, an introductory “A-school” curriculum has been developed and advanced “C-school” courses are planned. There is also a desire for just-in-time training (JITT), which consists of initial general training followed by shorter system-specific fine-tuning later.

Objectives: The goals of this work are to identify RW training gaps, methods, and performance metrics to:
(1) Update A-school
(2) Create C-school
(3) Enable JITT

Approach: Our approach focuses on RW Sailor mental models (MMs), i.e., cognitive representations of systems and how they work. In particular, we are investigating how to nurture MMs that enable Sailors to rapidly acquire and retain the knowledge, skills, and abilities (KSAs) needed to effectively operate and maintain unmanned machine-learned systems. We probe MM accuracy in interviews by obtaining trainee descriptions of system capabilities, limitations, control, troubleshooting, operations, and dependencies, which we grade by comparing to instructor documentation. Trainees also make performance predictions of system accuracy, networking, mechanics, controls, and problems. These are compared to performance outcomes at later live events. By probing MMs, we identify deficiencies in content and methods to enhance current A-school training, gaps in C-school training, and MMs that are similar across systems. This allows us to identify common concepts and KSAs to create scaffolding to enable JITT.

Results: Thus far, this effort has several findings. Current training efforts would benefit from more hands-on learning—trainees consistently desired this, and those who received no hands-on learning demonstrated poorer understanding and knowledge retention. Future C-school training could address identified KSA gaps in networking, machine-learned computer vision, and acoustics. Trainees consistently expressed frustration and inability to solve networking problems. Many did not understand common computer vision pitfalls, which affected trust and understanding of appropriate system use, and many repeatedly asked for guidance or demonstrated a lack of sound propagation and detection knowledge. For JITT, we identified a common scaffolding that includes KSAs related to setup, teardown, repair, and servicing; considerations such as endurance, terrain, and weather; operations such as controls, mission profiles, networking, and communications; and tactics, techniques, and procedures involving safety, troubleshooting, and survivability via detection avoidance.

Mental Models for Robotics Warfare Specialist Rating and Machine Learning Training

Compass

As naval operations increasingly rely on machine learning for combat decisions, sensor fusion, and autonomous systems, vendor lock-in threatens the Navy's ability to adapt ML capabilities across evolving threats and platforms. This presentation examines strategies for maintaining ML platform independence in Department of War procurement, addressing unique challenges in deploying machine learning systems for naval applications.

Machine learning platforms create dependencies through proprietary training frameworks, custom model formats, and specialized inference environments. Naval ML applications face amplified risks: classified training data, edge deployment on vessels with limited connectivity, real-time inference requirements for weapon systems, and mission-critical reliability constraints make vendor switching prohibitively expensive. When ML models drive targeting decisions, threat detection, and autonomous navigation, platform dependencies compromise operational flexibility and technological sovereignty.

We provide actionable strategies proven in naval ML deployments. Technical approaches include containerization for portable ML inference pipelines, ONNX for model interoperability across 1,700+ tools enabling seamless framework migration, MLflow for vendor-neutral experiment tracking across distributed teams, and Infrastructure-as-Code for reproducible ML training environments. Procurement reforms address modular contracting breaking ML projects into interoperable components, data rights ensuring government ownership of training datasets and model weights, and performance-based contracting focused on operational metrics rather than vendor-specific implementations. Implementation follows a phased roadmap validated at Naval Surface Warfare Centers.

Case studies demonstrate feasibility for naval ML systems. The Chief Digital and AI Office's Global Information Dominance Experiments deployed vendor-agnostic ML integration layers across all platforms. Naval Surface Warfare Centers successfully migrated computer vision models between cloud providers and edge devices. The Navy's "buying operational outcomes" approach prevents lock-in by procuring mission capabilities rather than specific ML frameworks.

We demonstrate that ML platform independence is both technically achievable and operationally essential for maintaining naval superiority in machine learning applications.

How the Department of War can Avoid Vendor Lock-In in the Procurement of AI Technologies

Rapid advancements in Large Language Models (LLMs) and Transformer-based capabilities are driving increased adoption of Artificial Intelligence (AI). These capabilities have the potential to offer transformative impacts on operator effectiveness and capability development, but their increased usage presents major challenges related to risk assessment, risk mitigation, and risk tracking. These adoption difficulties are further amplified in highly-regulated and compliance-driven industries, such as defense.
At the core of these difficulties are: (i) demand for and implementation of AI capabilities outpacing regulatory guidance and contractual requirements, (ii) poor compatibility between AI-specific risks and existing non-AI risk management and tracking methodologies, and (iii) the lack of quantitative measures for risk severity and composite risk weighting.
In response, we introduce a practical AI risk framework consisting of: (i) a standardized taxonomy of ten AI risk types spanning three top-level categories (Responsibility, Security, and Accountability), providing comprehensive coverage that can be systematically mapped to existing standards and governance artifacts, and (ii) an organizational risk calibration method that leverages semi-automated, scenario-based questionnaires and structured qualitative-to-quantitative mappings to derive scoring criteria and category weights for composite risk scoring. This approach is designed to integrate with existing risk management processes in highly regulated environments and to support consistent, repeatable evaluation of AI-enabled capabilities.

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