United States

The US Navy is committed to providing top-of-the-line care as required by SECNAV INSTRUCTION 3900.41H to its valued marine mammal assets. Recent advances in machine and deep learning have made it possible to predict various disease states in humans from voice recordings. Pattern analysis of dolphin whistles has traditionally relied on human operators to differentiate between call types and measure acoustic characteristics. By using machine and deep learning for audio analysis, we can reduce human bias and improve feature pattern detection. The sound and health team has created a vocal catalogue of individual whistle recordings from 20 of the Navy’s bottlenose dolphins. Additionally, an extensive health history database for each animal is maintained as part of a preventive medicine program. Together, these result in a unique, labelled dataset comprised of tens of thousands of whistles emitted during defined health states. These data were leveraged to train a gradient-boosted classifier to classify the health status of a dolphin from audio files of a recorded whistle. Preliminary results suggest that health information is encoded in dolphin whistle characteristics, similar to changes in the human voice. Applied goals for optimizing, testing and implementing this tool to aid in the early classification of illness in Navy dolphins are discussed. 

NAML 2023

Utilizing Machine Learning to Detect Health Changes in Navy Dolphins from Their Whistles

### 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. 
<br>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 7th annual workshop on Naval Applications of Machine Learning!

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.

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

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific<br><br>

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The 7th 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.


The future success of the Navy will depend, in part, on artificial intelligence. In practice, many artificially intelligent algorithms, and in particular deep learning models, rely on continual learning to maintain performance in dynamic environments. The software requires adaptation to maintain its initial level of performance in unseen situations. However, if not monitored properly, continual learning may lead to several issues including catastrophic forgetting in which a trained model forgets previously learned tasks when being retrained on new data. The authors created a new framework for safely performing continual learning with the goal of pairing this safety framework with a deep learning computer vision algorithm to allow for safe and high-performing automatic deck tracking on carriers and amphibious assault ships. The safety framework includes several features, such as an ensemble of convolutional neural networks to perform image classification, a manager to record confidences and determine the best answer from the ensemble, a model of the environment to predict when the system may fail to meet minimum performance metrics, a performance monitor to log system and domain performance and check against requirements, and a retraining component to update the ensemble and manager to maintain performance. The authors validated the proposed method using extensive simulation studies based on dynamic image classification. The authors showed the safety framework could probabilistically detect out of distribution data. The results also show the framework can detect when the system is no longer performing safely and can significantly extend the image classifier’s working envelope. Future follow-on work will include increasing the fidelity of the images to mimic views from the Integrated Launch and Recovery Television.

Assurance for Continual Learning Systems

Bats have evolved highly advanced biosonar systems that combine information encoding in the physical domain by virtue of complex ear motions with neural extraction of information. In order to emulate this behavior, prior research has approximated the dynamics of bat pinna using fixed deformation patterns, e.g., by virtue of a lever deforming a silicone replica of a bat pinna. However, these systems could capture little of the known and suspected variability in the pinna motions that may allow bats to adapt to different sensing situations in their natural environments. As a first step towards exploring the use of variability in the acoustic characteristics of the bat pinnae, we have designed a deep-regression network that can predict the
beampattern of deformed pinnae. To enable this, we have rigged a 3D tomographic reconstruction model of a hipposiderid bat’s (Hipposideros pratti) pinna with three tendons that were used to test 10 actuator states each. All resulting shape deformations were then exported into a frequency-domain BEM model which predicted the ultrasonic beampatterns as function of the actuator states. Finally, we have reproduced the BEM beampattern prediction with two deep neural network architectures: a deep radial basis function neural network and a multilayer perceptron neural network. Both architectures were able to reproduce the BEM results. Since inference is computationally much less costly than BEM simulations, these deep-learning models for the link between pinna actuation and acoustic properties can be used for finding ways to harness the variability in the bats’ pinna shapes using expensive training methods such as reinforcement learning.

Deep-Learning Models of the Dynamic Periphery of Bat Biosonar

For general-sum, n-player, strategic games with transferable utility, the COCO value provides a computable method to both 1) quantify the strategic value of a player; and 2) make cooperation rational through side payments. We give a simple formula to compute the COCO value in normal-form games. Next, we provide two methods to generalize the COCO values to stochastic (or Markov) games, and show that one of them may be computed using generalized Q-learning algorithms. Finally, an empirical validation is performed on stochastic grid-games with three or more players.

N-Player COCO-Q Reinforcement Learning

Cyclostationary features such as high-order moments and cumulants are often applied in detecting and classifying digitally modulated signals. However, these predictor variables may not be mutually uncorrelated, raising the concern that potential multicollinearity may lead to redundant information and significantly affect quality prediction. This work explores machine learning and statistical methods to mitigate multicollinearity issues. We first perform multicollinearity diagnostics by evaluating variance inflation factors and eigendecomposition of the correlation matrix of the predictor variables. To combat multicollinearity, we apply feature transformation using the principal component analysis (PCA), a traditional dimensionality reduction technique that has been well-studied and is suitable for small feature sets. Our empirical results show that the chosen diagnostic tools effectively detected cyclostationary multicollinearity. Future work will include assessing PCA against existing methods for selection of moments and cumulants.

Multicollinearity Diagnostics on Cyclostationary Features

Navy divers are exposed to health risks while operating in marine waters contaminated with a wide variety of bacterial, viral, and protozoan pathogens. Timely and accurate analysis of measured bacterial levels to predict illness is rarely possible due to the costs involved. Available data integrated through the means of knowledge graphs can be transformed into knowledge that can help divers avoid exposure to biological hazards. A knowledge graph (also known as an ontology) is a graph-based data model that can express and integrate structural knowledge from multiple domains in the form of (head-entity, relationship, tail-entity) triples, where the entities represent nodes in the graph and the relationships represent edges that connect entities. Knowledge graphs can encode complex rules which are useful in defining and reasoning about the semantics of the entities and can lead to new knowledge and discoveries. For example, given the following two triples in a knowledge graph (Pseudomonas aeruginosa, causes, acute respiratory infection), (acute respiratory infection, isa, communicable disease), one can infer that (Pseudomonas aeruginosa, causes, communicable disease). While knowledge graphs can be useful in inferring new knowledge, extracting triples from large-scale data manually can be a tedious task and the resulting graphs can suffer from incompleteness. Currently, there are many knowledge graphs or ontologies readily available, including a water quality ontology (DOCE), an epidemiology ontology (EPO), an infectious disease ontology (IDO) and an exposure ontology (ExO), among others. However, there is no knowledge graph that links water quality to health risks posed by water activities such as diving. To fill in this gap, we have focused on the construction of a knowledge graph that links water quality to health risks by leveraging existing ontologies and large-scale data available in PubMed. We constructed a knowledge graph based on semantic triples automatically extracted from the Semantic MEDLINE database (SemMedDB), a PubMed-scale repository of biomedical triples. The entities in the SemMedDB triples correspond to Metathesaurus concepts in the Unified Medical Language System (UMLS), specifically concept unique identifiers (CUIs), while the relations are drawn from the UMLS semantic network. Using a set of manually identified CUIs that we found to be most relevant to water quality and health risks, we constructed a graph of approximately 75,000 semantic triples which contain at least one entity from the manually identified set. We subsequently used the constructed knowledge graph to study machine learning approaches for knowledge graph completion (KGC), to help identify new valid triples or new relationships between existing entities. Specifically, we experimented with the KG-BERT approach for graph completion by employing pre-trained BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (Robustly Optimized BERT Pre-training Approach) models and also equivalent models fine-tuned on a collection of water quality and health-risks abstracts retrieved from the Web of Science. Experimental results showed that the KG-BERT approach with BERT/RoBERTa models fine-tuned on the domain-specific corpus improves the performance of KG-BERT with pre-trained models. Furthermore, the best results overall were obtained with a fine-tuned RoBERTa model, which has an accuracy of 88.61% on the valid triple classification task.

Leveraging Existing Literature and Machine Learning to Build a Knowledge Graph Focused on Water Quality and Health Risks

Recent operational challenges have prompted a renewed interest in mission-engineering tools that can advance decision-making and innovation in defense. These tools have the ability to simulate high-fidelity combat engagements to analyze operational concepts, platforms, systems, and capabilities in multiple domains to develop optimal solutions to the intricate and difficult issues within the field of mission engineering. One such tool is the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). AFSIM is a standard combat simulation tool that includes a variety of applications and plugins that can be used for systems engineering as well as engagement, mission, or campaign-level simulation. Due to software challenges, using AFSIM in combination with Reinforcement Learning (RL) poses particular challenges that must be overcome before RL will gain widespread use in the AFSIM community. This study proposes a novel way to utilize synthetic data created through AFSIM by using offline, or data-driven, RL.

Data-Driven Reinforcement Learning for Mission Engineering and Combat Simulation

"Rapid advances in Artificial Intelligence, Machine Learning, and Robotics have led to the unprecedented adoption and acceptance of machine intelligence in complex domains that were previously limited to human experts.  As these innovations have continued, especially in the area of neuro-symbolic intelligence, there has been a increased interest in cognitive capabilities in machine intelligence that will allow an AI system to function less like an application or tool, and more like an interdependent teammate.  Symbiotic Artificial Intelligence holds the promise of providing both autonomous and interdependent decision support to stakeholders in society with little or no technical experience, if AI systems can better adapt to users, explain their reasoning using emotionally vulnerable methods to build relationship equity and establish trust, and understand their purpose in a mission and task based manner, and their relationship with other performers human or machine.

In this work, we review the challenges that must be addressed in order to achieve Symbiotic Intelligence, and present some basic building blocks of a novel, open-source, cognitive architecture that has resulted from our work in this space.  Our methods are designed to be flexible, to interoperate with a variety of other AI and ML components, and have been demonstrated effectively in the field in both simulated crisis decision making contexts and during the COVID-19 crisis.  In this work we present: (1) the use of interdependence analysis methods and workflow orchestration to establish a task- and mission-based sense of self for the Symbiotic AI, and theory of mind for its co-performers; (2) a trust-based relationship equity model that evaluates trust- and trust-adjacent signals to understand the comportment and state of other performers; (3) a novel manifold learning technique that improves our ability to elicit commander's intent, refine, and iterate on tasks, and converge on loyal and performant behaviors; (4) a novel method for lexical entrainment that allows the extension of neuro-symbolic methods to lexical spaces, estimating the common ground with other performers; and (5) novel methods for convergent and divergent reasoning, using lexical commands, to generate strategic surprisal.  We envision these components not as a monolithic architecture, but as more of a services architecture to provide the necessary functions for interdependence analysis.  We discuss the theory behind our methods, their practical concerns, and the direct experimental results, and their use in human-subjects experiments that provide evidence of their efficacy, especially in crisis decision making."

Leveraging Manifold Learning and Relationship Equity Management to build Symbiotic and Explainable Artificial Intelligence

The Navy is looking for new machine learning methods to improve fleet management, conduct passive surveillance, and perform target identification. Conventional machine learning methods leverage neural networks, SVMs, or a random forest approach. These are not the only methods. Oracle has developed an industry-leading prognostic algorithm called the Multivariate State Estimation Technique (MSET) to perform prognostic analysis used in fleet management, passive surveillance, and target identification. Oracle’s algorithm can be deployed on-premises, in the cloud, or at the edge, and it can be delivered in a user-friendly package for operators without the need for formal training. MSET is an advanced prognostic pattern recognition method that was originally developed by the DOE for high-sensitivity prognostic fault monitoring applications in commercial nuclear power and aerospace applications. Over the last 20 years, Oracle has pioneered a suite of intelligent-data-preprocessing (IDP) algorithms and automated tuning and sensitivity-optimization algorithms for a second generation MSET2, which possesses significant advantages over conventional ML algorithmic approaches, including neural nets. MSET2 advantages include higher prognostic accuracy, earlier warning of incipient anomalies in complex/dynamic/chaotic time-series signatures, lower false-alarm and missed-alarm probabilities, and much lower overhead compute cost, which is crucial for real-time dense-sensor streaming prognostics. MSET2 has been used in a broad range of safety-critical, mission-critical, and business applications for anomaly detection, early warning, counterfeit-component detection, and passive surveillance. Applied examples include NASA space shuttles, naval ship-propulsion prognostics, Oil-and-Gas exploration predictive and prescriptive maintenance, human-in-the-loop supervisory control, prognostic cyber security for SCADA assets, Utility grid assets, and enterprise IT assets in commercial datacenters. 

Naval Prognostic Applications for MSET2 Algorithm

Current AI systems lack adaptability: They excel in well-defined, structured environments but become unreliable in highly dynamic ones. Novelty-robust AI aims to resolve these inconsistencies by focusing on methods that maintain performance under unexpected and unforeseen conditions. Despite its promising results, however, novelty-robust research is still removed from the open-world disruptions commonplace in military operations. Our objective is to evaluate the effectiveness of this research on existing military simulations. The contributions of this presentation include an explanation of different novelty-robust planning prototypes, a comparison of their approaches and integration requirements, and a report of each prototype’s results.

Novelty-Robust AI in Military Domains

The presentation will provide an overview of what is ChatGPT.  There is much talk in the media about ChatGPT, the generative pre-trained transformer chatbot, which was launched in November 2022 by openAI. The talk will also introduce openAI as well as potential usecases of ChatGPT.  A demonstration of ChatGPT will also be provided.   

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Assurance for Continual Learning Systems

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