Current Research Topics for the
DEVCOM Analysis Center Broad Agency Announcement For Applied Research
W911NF-23-S-0003
Disclaimer
All current DEVCOM Analysis Center (DAC) research topics can be found at: https://www.army.mil/article/261533. Changes to these topics will be made using the website on an as needed basis. DAC will consider whitepapers and proposals that may not directly align to a topic published by a DAC Technical Point of Contact (TPOC), but can demonstrate a strong alignment to DAC’s mission and core competencies listed within Appendix A. This document is a printed copy of the current DAC research topics as of the noted print date. DAC maintains a daily static snapshot of the DAC research topic website to ensure submissions are aligned with listed research topics on the day of submission. Interested parties are encouraged to continually browse the DAC research topic website and review the DAC Broad Agency Announcement (BAA) for instructions on submissions.
The DEVCOM Analysis Center Broad Agency Announcement for Applied Research,
W911NF-23-S-0003,
is available on https://www.grants.gov/ and https://sam.gov/
DAC’s research objective is to gain knowledge on phenomenon or technology to account for its effects within the analysis and assessment process. The knowledge and understanding gained through research will be used to model and analyze the performance, systems effectiveness, and human-systems integration of new phenomena or technologies.
Specific areas of knowledge and understanding:
- Military application of new phenomena and technologies to develop algorithms to model system performance.
- Potential threat application of new phenomena and technologies to develop tools and techniques to assess US system vulnerabilities and system effectiveness.
- Human aspects of new phenomena and technologies to aid Human-System Integration (HSI) assessments and modeling.
- New modeling & simulation approaches and techniques for system performance, HSI, and sustainment.
- Identification of credible data needed for algorithms to model system performance.
- New experimentation approaches and techniques to assess systems.
Title: Methodologies and Techniques to Analyze Assistive Automation (AA) & Artificial Intelligence (AI) enabled systems
Announcement ID: DAC BAA-001
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
Title: Scientific Understanding and Analytical Methodology for Multi-Domain Operations (MDO)
Announcement ID: DAC BAA-002
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
Title: The Science and Analysis of Systems of Systems (SOS)
Announcement ID: DAC BAA-003
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
Title: Analytical Methodology for Future Army Systems Enabled by Quantum Technology (Quantum)
Announcement ID: DAC BAA-004
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
Title: Artillery - Hypersonics Data and Analysis
Announcement ID: DAC BAA-005
TPOC: David Fordyce, david.f.fordyce.civ@army.mil
Title: Network Vulnerability and Effects Assessment Methodology (N-VEAM)
Announcement ID: DAC BAA-006
TPOC: Dr. Jayashree Harikumar, jayashree.harikumar.civ@army.mil
Title: Cybernetic Systems (Inferring Human Intent for Seamless Human-Artificial Intelligence Interactions)
Announcement ID: DAC BAA-010
TPOC: Dr. Norbou Buchler, norbou.buchler.civ@army.mil
Title: Humans in Multi-Agent Systems
Announcement ID: DAC BAA-011
TPOC: Dr. Norbou Buchler, norbou.buchler.civ@army.mil
Title: Human Systems Integration (HSI) Modeling and Analysis
Announcement ID: DAC BAA-012
TPOC: Andrew Bodenhamer, andrew.s.bodenhamer.civ@army.mil
Title: Sustainment Performance and Mission Success Impacts
Announcement ID: DAC BAA-017
TPOC: Dr. David Mortin, david.e.mortin.civ@army.mil
Title: Using Machine Learning and Open Source Big Data Platforms to Enable Prognostics and Predictive Maintenance
Announcement ID: DAC BAA-020
TPOC: Mr. Scott Kilby, Thomas.s.kilby.civ@army.mil
Title: Modeling & Simulation (M&S) Tools and Algorithms to Assess Electromagnetic Pulse (EMP) Vulnerability, Survivability, and Mitigation / Shielding
Announcement ID: DAC BAA-022
TPOC: Mr. Robert Hessian, Robert.t.hessian.civ@army.mil
Title: Personnel Survivability
Announcement ID: DAC BAA-024
TPOC: Dr. George Steiger, george.e.steiger.civ@army.mil
Title: Methodologies and Techniques to Analyze Assistive Automation (AA) & Artificial Intelligence (AI) enabled systems
Announcement ID: DAC BAA-001
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
DAC Office: Office of the Chief Scientist
Domain: Data & Analysis, Foundational Research, Lifecycle Engineering
DAC Foundational Research Competencies: Understanding new phenomena or technologies; Understanding military application of new phenomena or technologies; Understanding Human-system integration aspects of new phenomena or technologies; new modeling and simulation approaches and techniques; New experimentation approaches and techniques.
Army Modernization Priorities: Network/C3I
Keywords: assistive automation, artificial intelligence, analysis, framework, modeling and simulation, data science, machine learning, decision aids, emergent behaviors
Description:
DAC is developing an analytical framework and tools to assess a broad range of AI-enabled systems & technologies. Current tools & data are not suitable to analyze AI-enabled systems, because they don’t address decision spaces and cycles and are limited in the information and decision sciences. This analytic framework would encompass all applications of AI to Army technologies and systems to include AI support to automation, autonomous maneuver, decision aids, and object recognition. The following types of analysis would be included in the framework: performance, system effectiveness, vulnerability (kinetic, electronic warfare, and cyber), Human-Systems Integration, and reliability.
DAC does not develop AI capabilities or algorithms to be used in Army systems but rather develops the analytic tools to assess and evaluate AI-enabled Army systems. Many projected Army capabilities, technologies, and systems will exploit AA/AI. Unfortunately, the treatment of AA/AI systems has tended to one of two extremes. Often the systems are treated as black boxes where systems, and Soldiers’ lives, are entrusted to machine learning (ML) algorithms that have been trained and tested over data sets that have sometimes been of dubious provenance. The other extreme is to directly assess the quality of the ML algorithm being used as well as the adequacy of the training and testing data sets. Neither of these extremes support scientifically informed decision making about the desired performance and system effectiveness of AI-enabled systems and technologies. DAC requires new tools that assess the AI-integration at the systems level, human-systems integration level, and at an operational level.
These new tools will answer more complex questions than the black box approach permits, and thus provide the Army a significant leap forward in analysis techniques, methodologies, and mindsets by focusing at the area where all weapons, mission command, and other systems converge and merge their operations with network connections. The Army must identify, articulate, and develop new and advanced analysis methods that can gauge how well proposed Army AI-enabled systems are taking advantage of new state-of-the-art AI technologies, mitigating new threats posed by advancing adversary capabilities, and adapting to radically new contested environments.
The new methods and techniques must consider credible mission context and the environments in which the systems will operate with a complex, systems-of-systems environment.Utilizing modern research in AA/AI, simulation, simulation languages, emerging mathematical techniques, and human factors analysis advancements will improve capabilities to explore the continuum of military operations. The scientific techniques and new analysis methodologies will address the complexity inherent in interactions between systems, including humans and multiple, autonomous, and potentially intelligent learning machines. Analyses will also explore the rapid growth of adversary capabilities, in areas such as cyber and electronic warfare, due to advancing commercial technologies.
Strategic opportunities within AA/AI analysis include research in the following areas:
- Understanding new AI technologies and approaches to be able to analyze and assess those technologies.
- Quantifying the performance and systems effectiveness of AI-enabled systems and technologies.
- Understanding the vulnerability of AI-enabled systems and how to assess the level of the vulnerability.
- Understanding Human-system integration aspects of AI technologies to assess their effectiveness including assessing human/machine interfaces and considering cognitive decision making processes.
- Computational science on advanced mathematical, statistical, and modeling and simulation techniques, such as predictive sciences, uncertainty quantification, advance computational algorithms, and data intensive techniques to model and assess AI-enabled systems.
- New experimentation approaches and techniques to assess AI-enabled systems.
- Consideration of emergent behaviors, associated failure modes and mechanisms, and reliability associated with AI-enabled complex systems.
Title: Scientific Understanding and Analytical Methodology for Multi-Domain Operations (MDO)
Announcement ID: DAC BAA-002
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
DAC Office: Office of the Chief Scientist
Domain: Data & Analysis, Science & Technology, Lifecycle Engineering
DAC Foundational Research Competencies: Mission Effectiveness Analysis, Decision Support
Army Modernization Priorities: Network/C3I
Keywords: multi-domain operations, MDO, framework
Description:
National leaders have long been concerned that the Army is losing overmatch. The loss is driven largely by leap-ahead technology gains by near-peer adversaries. Army Senior Leaders (ASL) responded to these challenges by recognizing that improved systems by themselves are no longer sufficient to obtain and maintain overmatch. To fight and win in anticipated future operating environments (FOE) novel warfighting concepts are also required. One such concept is Multi-Domain Operations (MDO). To flesh out the details of MDO, ASL introduced additional new terminology for describing the future fight. The new terms, used in the Army Modernization Strategy (AMS) and elsewhere, include calibrated force posture, multi-domain formations, strategic maneuver, deep maneuver, competition, contested spaces, convergence, cross-domain, cross-domain fires, cross-domain maneuver, synergy, decisive space, dislocate, dis-integrate, and others. In some cases traditional terminology has been re-defined; for example, “battlefield” now includes all domains (air, land, space, and cyberspace, the electromagnetic spectrum, and the information environment).
Making useful quantitative scientific assessments of the many aspects of the MDO environment is a new challenge for Army RDT&E. The Army needs a new scientific picture that adequately reflects the complex systems and technologies including cyber and electromagnetic activities, swarming munitions, autonomous systems, and human-agent teaming. The new picture will be underpinned by a mathematical framework of complexity and emergence theory and will integrate physics and engineering-based models with appropriate tactics, techniques, and procedures. The new scientific picture will also include advanced data analysis tools, which will generate the knowledge required by decision makers and others to fully assess the causes and implications of system performance within complex, multi-domain, and operationally relevant environments. Required analysis tools include appropriate measures of effectiveness and associated metrics of performance, as well as unique ways to visualize the results and intuitively appreciate the impacts of various ways of operating across domains.
Specific strategic opportunities within MDO analysis include:
i. Research into quantifying calibrated forced posture. This will require identifying the dominant observable variables that drive proper force calibration for both friendly and enemy forces. It will also require consideration of socio-cultural, and other non-traditional methods for nullifying effective command and control and reducing force effectiveness.
ii. Research into picturing and portraying a friendly multi-domain formation in a fashion that is simultaneously accurate, intuitively clear for Commanders and their staffs, and sufficiently robust that it is clear when the friendly formation needs to be supplemented or otherwise modified.
iii. Research into sound quantification of the (doctrinally) novel concepts of competition and convergence. For example when are two competitors equal? When have US forces pursued competition effectively? What are appropriate combinatorial tools for assessing the mathematics of convergence? What are appropriate analytical and M&S means for objectively analyzing and assessing a given state of competition and the associated calibrated force structure?
iv. Research into the many cross-domain synergies that are postulated by MDO doctrine. What are the appropriate mathematical tools to quantify cross domain outcomes, for example, when cyber-attacks, kinetic attacks, and EW jamming are applied simultaneously? How should we calculate the synergy gained in such cross-domain attacks? How do we determine the appropriate geospatial boundaries for cross-domain consideration?
Title: The Science and Analysis of Systems of Systems (SOS)
Announcement ID: DAC BAA-003
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
DAC Office: Office of the Chief Scientist
Domain: Data & Analysis, Foundational Research
DAC Foundational Research Competencies: Mission Effectiveness Analysis, Human Engineering and Performance, Cyber Resilience
Army Modernization Priorities:
Keywords: model and simulations, methodologies, framework, analysis, systems of systems
Description:
Information dependencies among disparate elements of the Army force are increasingly a feature of contemplated Future Operating Environments (FOE). The optimum target for a platoon might be selected by a corps asset that is dozens or even hundreds of kilometers away. This kind of dependency implies that any adequate understanding or analysis of the Army SOS must avoid some of the traditional assumptions which traditional Army analysis has made. One such assumption, which has not always been clearly stated by Army scientists and analysts, is that events taking place in different parts of the battlefield may usefully be regarded as independent events. Although the assumption has never been mathematically or scientifically valid, it did yield reasonable approximations of combat outcomes when the FOE of interest were, for example, meeting engagements in the Fulda Gap. For cross-domain warfare, the independence assumption has nothing to recommend it.
What is now needed are methodologies and tools to characterize the performance of systems of systems at the engineering level so that the exact interactions between and across Army systems can be accurately portrayed in Army M&S and analysis. This level of detail is required to support the full range of planned capabilities in the FOE. The new M&S and analysis capability is crucial as the Army assesses future technology and system tradeoffs. The new tools must rise above the “eaches” and illuminate the effectiveness of competing capability SOS packages and technologies rather than just individual systems. To fully realize this capability requires new engineering metrics (e.g., mission success or failure, Soldier and equipment survivability, weapon systems lethality, and cyber and electronic warfare for friendly and enemy forces and the quality and throughput of network operations) while developing algorithms that can be validated and used with approved scenarios.
Specific strategic opportunities within SOS science and analysis include:
i. Development of engineering-level methods for quantifying the performance of the SOS. Research on the question of when, if ever, the traditional assumption that all events are independent may retain validity in the SOS context. How should we assess the sustainment needs of the SOS beyond the needs of the component systems?
ii. Analysis of Soldier-SOS interactions will presumably require different analytical tools and models than traditional Human Systems Integration (HSI) analysis of Soldier interaction with a single system. How should we characterize the specific requirement for such tools, and how should we set about developing them?
iii. Research on whether there are simplified (i.e. non-engineering level) methods of assessing SOS performance that may yield acceptable quality results for at least some classes of cases.
iv. Research and development of improved technical capability to analyze the resilience of Army systems when other systems in the SOS are not available. As the number of cross-system dependencies increases, this challenge becomes more and more important. How should we assess the new vulnerabilities that arise because of these SOS dependencies?
v. Research on tactically relevant packages of Army communications systems and collateral technologies at varying levels of fidelity. The Army needs to understand when an engineering level of fidelity is required and when cruder and less detailed approximations may suffice.
Title: Analytical Methodology for Future Army Systems Enabled by Quantum Technology (Quantum)
Announcement ID: DAC BAA-004
TPOC: Dr. Thomas Stadterman, DACBAA@army.mil
DAC Office: Office of the Chief Scientist
Domain: Data & Analysis, Science & Technology
DAC Foundational Research Competencies: Mission Effectiveness Analysis
Army Modernization Priorities: Networks
Keywords: quantum, analytics, PNT, cryptography
Description:
Quantum technology has progressed from an idea many (including Albert Einstein) regarded as spooky in the 1930s to practical laboratory demonstrations of the potential utility of entanglement and other quantum phenomena. Enormous sums of money are being spent in the US and abroad to move these laboratory demonstrations into practical technologies. Experts disagree on how long it might take for the remaining barriers to be overcome in such a way that it would be practical for the Army to use quantum technology enabled systems in tactical environments. Despite this disagreement, it is prudent for the Army analytical community to think about tools for evaluating the leap-ahead gains that may be available with quantum technologies. This analytical exercise is critical because the potential gains are not merely percentage improvements but rather capabilities that can fundamentally change essential aspects of ground combat in favor of the side with the quantum solutions.
There is a rough consensus that there are four areas where quantum breakthroughs might make dramatically improved technologies available to Army warfighters. These potential breakthrough areas are: computing and simulation; communications and networking; sensors and imaging; and position, navigation, and timing (PNT). DAC must begin developing tools for analyzing quantum systems that enable entirely new Army capabilities. The specific opportunities mentioned below are representative of the kinds of questions Army analysis will need to be equipped to answer when these technologies begin to be realized.
Representative research opportunities within Quantum technology analysis include:
i. If a quantum computer allowed realization of all binary states at once rather than one at a time, what new simulation applications might be practical for battlefield use by current Army systems? Could the ability to solve classically insoluble cryptographic problems allow Army systems to exploit enemy networks on the battlefield?
ii. If quantum communications and network tools allowed unbreakable quantum key distribution (QKD), how would this facilitate both tactical battlefield communications and support of tactical operations with national assets?
iii. What options would quantum PNT offer to tactical commanders using current systems, especially during periods when GPS is denied? What potential new vulnerabilities would be introduced if the Army were dependent on atomic clocks for timing? Which Army systems could exploit quantum clocks with better-than-GPS timing for tactical advantage, and how should we quantify that advantage?
What new analytical techniques or M&S will be required to assess the effectiveness of the nearest term quantum inertial sensors like accelerometers and gyroscopes? If very broad-band quantum RF sensors (Rydberg) are incorporated into sensors for detection in difficult (tunnels, bunkers) environments, what new measurement or M&S capability will be required for effectiveness assessment? What existing EW systems could benefit from this type of technology? Is our current suite of measurement and M&S tools sufficient to assess the effectiveness of quantum EW capabilities?
Title: Artillery - Hypersonics Data and Analysis
Announcement ID: DAC BAA-005
TPOC: David Fordyce, david.f.fordyce.civ@army.mil &DACBAA@army.mil
DAC Office: Combat Systems Analysis
Domain: Science & Technology, Data & Analysis
DAC Foundational Research Competencies: Integrated Materiel Performance, Ballistics Analysis
Army Modernization Priorities: long range precision fires, soldier lethality, air and missile defense
Keywords: hypersonic, materials, ballistics, analysis
Description:
Development of weapons traveling at hypersonic speeds by near-peer and other countries requires a response by the Army – both in protecting U.S. assets, personnel, and the Homeland, and development of our own hypersonic offensive capability. In order to successfully accomplish these goals, the physics and impacts of these weapons (glide body, kinetic projectiles, ramjets and scramjets) traveling greater than Mach 5 close to the ground must be clearly understood. This understanding requires data and analytic methods to fully describe the effects of the environment on the vehicle in flight, and the weapon/target interaction (terminal ballistic conditions).
In flight: To understand the structural response in flight through terminal target engagement, material models are required considering 1) rate effects, 2) strain hardening, and 3) thermal softening. There is almost no data in the hypersonics community which includes thermal softening. Other researchers can attest to this. Most tensile & split-Hopkinson bar experiments are performed at a reference temperature. These characterizations are needed on a span of temperatures that approach the melting temperature of the projectile skin. In addition to material modeling, understanding the ability of the munition to maneuver at these speeds is critical.
Weapon/Target interaction: To understand the effect of the hypersonic vehicle on ground-fixed (structures/bunkers), ground-mobile (tanks, military or other vehicles), or maritime targets (ships/underwater vehicles), the terminal ballistic conditions must be known. This includes phenomena such as striking/penetration and mass/shape of fragments or single large mass projectiles, synergistic effects such as blast/fragmentation, ground shock, or cross-domain effects such as an electromagnetic pulse created by the weapon functioning.
Analysis of these weapons requires a multi-disciplinary approach and assessment/extension of existing models or creation of new ones into the hypersonic realm. Development of appropriate data of the effects of these weapons will likely require experimentation, as well as intelligence of the technologies of what near-peer and other nations have developed/are developing.
(Page Intentionally Left Blank)
Title: Network Vulnerability and Effects Assessment Methodology (N-VEAM)
Announcement ID: DAC BAA-006
TPOC: Dr. Jayashree Harikumar, jayashree.harikumar.civ@army.mil & DACBAA@army.mil
DAC Office: Cyber
Domain: Data & Analysis
DAC Foundational Research Competencies: Cyber Resilience, Electronic Warfare (EW) Threat Defense
Army Modernization Priorities: Network/C3I
Keywords: electromagnetic warfare, cyber, vulnerabilities, CEMA, artificial intelligence, machine learning, Li-Fi, communications
Description:
The goal of this research area is to develop analytical methodology and capabilities to characterize hardware and software that enable Electromagnetic Warfare and Cyber capabilities to assess operations of Army Network and communication platforms and dismounts, while maintaining freedom to maneuver, communicate, and sense. This endeavor strives to develop a holistic cross-domain analysis and assessment methodology for network and communication technologies faced with advanced Cyber Electromagnetic Activity (CEMA). These investigations are critical to identifying vulnerabilities of United States systems and technologies so that network and network-enabled systems can be hardened as early in development as possible.
Core applied research areas include:
i. Vulnerability (Intrusion) Detection using AI and ML: analyze in a lab environment implementations of Army relevant AI ML systems, their vulnerabilities and mitigation techniques.
ii. Personal Area Networks: focuses on high bandwidth using military applications (e.g. Wi-Fi, Bluetooth).
iii. Cybersecurity of Systems on Cloud: research on emerging cloud technologies to improve the cybersecurity posture of cloud systems. This project looks to create cloud testbeds for the new technologies, proof of concepts, use cases, prototypes, tools, and methodologies to enhance the cyber analysis and assessment of cloud technologies considered for fielding. This applied research program will engage with experts from partner universities.
iv. LiFi Cyber: focuses on identifying protocol vulnerabilities on LiFi devices.
v. Cellular Communications: develop scenarios to conduct controlled experiments on a simulated network communications backbone;
vi. Adversarial AI: look to create Adversarial AI proof of concepts, use cases, prototypes, tools, and methodologies to support the analysis of AI technologies being fielded; engage with Machine Learning experts from partner universities on Army relevant areas such as autonomous systems, image recognition assistive robots, network security, etc.
Results from this research will inform DEVCOM DAC, DEVCOM C5ISR, DEVCOM ARL, PEO C3T & Network Cross Functional Team. The expected payoff is the establishment of critical analysis elements that leads to a well-designed measure of merit and development of an analysis and assessment database. Ultimately, this will affect the quality of CEMA future assessments, which is critical to the development of a robust modernized Army network.
Title: Cybernetic Systems (Inferring Human Intent for Seamless Human-Artificial Intelligence Interactions)
Announcement ID: DAC BAA-010
TPOC: Dr. Norbou Buchler, norbou.buchler.civ@army.mil & DACBAA@army.mil
DAC Office: Human Systems Integration
Domain: Data & Analysis
DAC Foundational Research Competencies: Human Engineering and Performance
Army Modernization Priorities: Network/C3I
Keywords: data collection, artificial intelligence, machine learning, analysis, cybernetics
Description:
This area focuses on developing a standard data-service, analytics, and inference engine of human intent based on repository of continuous human-machine interactions and workflows to enable seamless collaborative decision-support by AI systems and advanced command by intent relationships. Currently, there is no standard collection or analysis of human-system interactions data-streams (mouse, key, application use) that can be leveraged across networked systems for human-system measurement, analysis, and adaptations.
The nature of work across multi-domain operations (MDO) is constantly changing due to an adaptive adversary, evolving threats & concepts of operations, and the blistering pace of technological change to include tools and systems that are increasingly capable of autonomy with AI technologies. A major focus within all domains is the interaction between human operators and their tools. The complexity of modern AI limits our ability to intuit the impacts of using new technologies. Almost all domains have extensive digital collections as networked systems regularly collect large system repositories of logged human-computer interactions (HCI). Such data-streams have largely been ignored and not leveraged into system engineering requirements for advanced capabilities. From a cybernetics perspective (e.g. regulation of human-AI systems), human-activity data-streams can provide continuous and virtuous feedback loops between human and AI system to effectively achieve ongoing system adaptations for decisive conjoined actions. We seek to leverage current methods and analytical approaches that leverage these HCI data-streams to improve the definition and resolution of operator and system performance to support DoD AI technological maturation (data-driven metrics/benchmarks), enable virtuous human-AI feedback-loops, and develop command by intention capabilities (machine learning classification).
Core research areas include:
i. Continuous Monitoring of Human-AI Systems: current acquisition strategies involve discrete operational test events to assess fit and functionality. This overlooks the impacts of changing operating conditions, workforce, adversaries and AI models. This core research area introduces instrumentation that will accommodate continual monitoring and analysis of human-machine teaming as a common data-stream standard.
ii. Human-Systems Task Analysis: develops capabilities for the collection and analysis, playback, search/filtering, and machine learning (ML) automation of operator and AI system activity, tool use & interactions, and performance metrics to support after action review (AARs) and test & evaluation in developmental & training exercises. The platform will support both real-time data analytics over the course of a scenario experiment/exercise as well as post-hoc/ playback data analysis.
iii. Command by Intent: the derivation/prediction of operator workflow with machine-learning classifiers can improve and supplement traditional assessment methodology, and also enable the human-system feedback loops and continuous optimizations necessary for adaptive AI, tailored decision-support to human operators, and command by intent relationships with AI systems. This research area is critical for multi-domain technologies in the C5ISR domain and has wide ranging implications for how we design and incorporate human agency into all high-risk actionable AI-supported work domains.
The near-peer great powers competition will be dominated by the armed forces most able to command advances in artificial intelligence. Communist China embodies centralized decision-making and batch execution (command by planning), whereas the U.S. embodies a more advanced command by intent Mission Command doctrine by delegation of purpose. Developing AI systems capable of dynamic command by intent capabilities within and across multi-domain operations (MDO) necessitates that our machines are purposively aligned with high-level information fusion of operator task-level workflows. Such data-feeds require a highly-contextualized representation of human workflows (tactics, techniques, and procedures) with continuous machine-level inference (and alignment to) derivations of human intent. This could prove to be a decisive-edge for agile and adaptive conjoined human and machine (AI) optimizations to counter debilitating first-strike capabilities and cyber (advanced persistent threat: APT, zero-day) threats.
Title: Humans in Multi-Agent Systems
Announcement ID: DAC BAA-011
TPOC: Dr. Norbou Buchler, norbou.buchler.civ@army.mil & DACBAA@army.mil
DAC Office: Human Systems Integration
Domain: Data & Analysis
DAC Foundational Research Competencies: Human Engineering and Performance, Cyber Resilience
Army Modernization Priorities: Network/C3I, soldier lethality
Keywords: Human-machine teaming, artificial intelligence, machine learning, decision aides
Description:
This area focuses on providing the critical technological breakthroughs needed for future Army multi-, mixed-agent teams across distributed network systems. These technologies must effectively merge human and agent capabilities for collaborative decision making and enhanced team performance; ensure that diverse teams of Soldiers comprehend new and critical information to maintain unprecedented situation awareness; and interact effectively with Soldiers and noncombatants to foster trust and gain community acceptance and influence across cultures and within complex, dynamic, politically sensitive environments. These complex multi-agent networked teams will enable faster and better informed decisions; reduce Soldier workload; provide otherwise unachievable levels of situation understanding and management; and maintain strategic and tactical advantages in future operating environments requiring the integration of cyber-human-physical dimensions.
The objective of this research is to provide the critical technological breakthroughs needed to shape current and future networked operational environments consisting of Army multi-agent, mixed-agent (humans, robots, associate agents, and intelligent systems) in distributed network systems to: (1) effectively enable teaming among human, robotic, and mixed-agent capabilities for collaborative decision-making and enhanced team performance in dynamic, and complex socio-technical environments; (2) ensure that diverse teams of Soldiers comprehend new and critical information to achieve unprecedented situational awareness, while maintaining optimal information burden on individuals; and (3) interact effectively with Soldiers and noncombatants in civil military scenarios to foster trust and gain community acceptance across cultures, and within complex, dynamic, and politically sensitive environments; and visualize and understand the complex dense urban environment battlespace and the diverse socio- cultural dynamics in a dense urban environment to allow Soldiers to effectively execute mission plans, sustain military forces, predict population behavior, and provide humanitarian support to civil populations.
Core research areas include:
i. Social Sensing of Human-Agent Teams: social sensing platforms for empirically-based behavioral analysis of small team networked interactions from a variety of digital and analog sensors with automated, scalable approaches flexible enough to store variable datatypes stemming from a plethora of data sources. This include pre-processing vast amounts of noisy digital and sensor data-streams to distill key human-system performance meta-data.
ii. Diagnostics of Socio-Technical Systems: teamwork diagnostics and intelligent decision-support capabilities to track, manage, and significantly impact team readiness and performance with a focus of understanding their interaction with AI tools and autonomous systems, with an emphasis on mission command and the cyber domain.
iii. Socio-Cultural Influences: captured in virtual agents for training and operational contexts, such as supporting civil military applications that leverage open-source intelligence to gain understanding of the socio-cultural dimensions for mission application and to combat emerging cognitive warfare capabilities.
These research results will apply network science approaches to inform interface design for mission command and distributed and collaborative decision making with heterogeneous AI and autonomous systems, maintaining unit readiness, as well as informing organizational design and tools for an applied understanding of the socio-cultural dimensions to the mission.
Title: Human Systems Integration (HSI) Modeling and Analysis
Announcement ID: DAC BAA-012
TPOC: Andrew Bodenhamer, andrew.s.bodenhamer.civ@army.mil & DACBAA@army.mil
DAC Office: Human Systems Integration
Domain: Data & Analysis
DAC Foundational Research Competencies: Human Engineering and Performance, Cyber Resilience
Army Modernization Priorities: Soldier Lethality
Keywords: human-machine teaming, artificial intelligence, machine learning, decision aides, simulation
Description:
The HSI Modeling and Analysis opportunity focuses on developing effective models and methodologies for predicting and enhancing human, system of system, and mission capabilities throughout the acquisition cycle. These efforts concentrate on identifying human capabilities and limitations within physical, perceptual and cognitive areas to develop and inform methodologies, novel tools, visualizations, interfaces, and decision aids; integrate the HSI domains; and develop technical, parametric system assessments.
The inclusion of physical, perceptual, and cognitive analyses to determine early, cost effective insertion of HSI criteria, capabilities, requirements, and performance specifications within the acquisition process will optimize Soldier-System performance and reduce overall program cost at the system of systems level. Application of these HSI methodologies will provide insight into the Soldiers’ capabilities and operation of systems and will highlight issues and gaps that need to be addressed to achieve an overall balanced system design. The deployment and utilization of these tools and methodologies must keep pace with increasingly advanced and shortened program acquisition schedules. Additionally, human performance data crucial to supporting development of acquisition requirements needed to assess future systems must be updated to apply to current and future technologies, such as the inclusion of manned-unmanned teaming and artificial intelligence. Studies and the resultant human performance data to support development of acquisition requirements, specifications and early system analyses needs to be performed. Practical design, analysis, and operational support tools need to be developed, deployed, and refined so that HSI practitioners can make effective use of the human performance data to achieve the goal of optimizing Soldier-System performance and minimizing cost in emerging system development efforts.
The Human System Modeling and Analysis focus area investigates and advances the sciences relevant to two performance challenges: 1) identifying Soldier performance tradeoffs on mission demands, environment, human characteristics, equipment and technology; and 2) understanding human factors that include sensing, perceptual and cognitive processes, ergonomics, biomechanics, and the tools and methodologies required to manage interaction within these areas and within the required capabilities for future Army warfighting functions. In developing and updating models, tools, and techniques capable of predicting and enhancing human, system and mission capabilities, four research foci will be emphasized:
i. Identify, develop, and apply human performance measures of effectiveness and human figure modeling tools.
ii. Integrate Human Factors, Mathematics, Statistics, System Engineering, to generate complex and critical tasks combinations that provide the necessary analytical data to support physical, perceptual and cognitive workload assessment and trade space analysis.
iii. Develop digital engineering approaches to represent the Soldier as a system while considering physical effects, cognitive load, and demographic influences in the context of Soldier Systems Integration.
iv. Conduct research to identify HSI capabilities, and develop requirements and performance specifications early on in the acquisition cycle for All Domain Protection required capabilities to: increase situational awareness and understanding, assess and improve individual readiness, lighten soldier physical and cognitive workloads, infer and aid in decision making, understand the operational environment, counter obstacles and hazards, and provide command and control to converge protection effects across all domains.
In performing system design analyses with integrated complex human performance behavior, Human Systems Modeling and Analysis aims to shape technology development and system design for the Future Force.
Title: Sustainment Performance and Mission Success Impacts
Announcement ID: DAC BAA-017
TPOC: Dr. David Mortin, david.e.mortin.civ@army.mil & DACBAA@army.mil
DAC Office: Sustainment
Domain: Lifecycle Engineering
DAC Foundational Research Competencies: Decision Support, Reliability, Availability, and Maintainability
Army Modernization Priorities:
Keywords: supply chain, sustainment, data analytics
Description:
Most of an Army weapon system’s life cycle costs are associated with sustainment (approximately 70%). With accelerated schedules to meet Warfighter’s needs, the Army assumes more risk for sustainment, reliability, and mission success. This area focuses on the development and refinement of the modeling and simulation that will provide Army decision makers the link between weapon system performance (as a system of systems), mission success, and the long-term sustainment and readiness impacts that we must budget for. This area will leverage current initiatives that have formed a foundation for the necessary data analysis, visualization, and trades structure.
This research space aims to understand how sustainment parameters affect overall system performance and what decisions need to be made to optimize sustainment positioning in order to maximize overall longstanding system performance and availability. The ability to plan and assure supply for manufacturing and sustainment drastically changed standard business processes.
What factors, parameters, and data points are needed to understand the overall supply and sustainment posture? Which are the most critical to the overall system? How can we model that system in order to project impacts and help inform decision makers?
(Page Intentionally Left Blank)
Title: Using Machine Learning and Open Source Big Data Platforms to Enable Prognostics and Predictive Maintenance
Announcement ID: DAC BAA-020
TPOC: Mr. Scott Kilby, Thomas.s.kilby.civ@army.mil & DACBAA@army.mil
DAC Office: Sustainment
Domain: Data & Analysis, Lifecycle Engineering
DAC Foundational Research Competencies: Decision Support
Army Modernization Priorities: Next Generation Combat Vehicle
Keywords: data analysis, predictive maintenance, efficiency, logistics, internet of things
Description:
The U.S. Army’s intention is to implement prognostics and predictive logistics (PL)for their ground vehicle fleet. PL can have many benefits including informing the vehicle crew of impending vehicle system failures, informing the unit commander that they may lose capability of one of their vehicles, and informing the unit level and fleet level logistic support organizations that a specific part needs to be ordered. In addition, prognostics and predictive analytics helps to ensure that the maintenance/sustainment organization is better prepared to efficiently conduct the repair of vehicles since they can plan and schedule the future maintenance event which provides greater vehicle readiness and ensures mission success due to awareness of current and future health condition of the vehicle fleet.
Predictive maintenance technologies and methods have been successfully applied across manufacturing industries, electrical power generation, and more recently for commercial vehicles over the last 40 years. The U.S. Army is wisely following these industries in the application of predictive maintenance to improve the efficiencies and effectiveness of their maintenance and logistic organizations.
Successful commercial industries are utilizing the latest wave of technology known as Industrial Internet of Things (IIoT) to implement predictive maintenance capability at their facilities and for their critical mobile assets. The IIoT four key technologies include edge devices, cloud computing, telematics, and machine learning analytics. All four of these technologies have been implemented by the Army with varying levels to enable predictive maintenance with the exception of telematics. Though telematics is one of the key technologies to enable data driven fleet management it has been the most difficult to implement for the Army, due to cyber concerns, implementation costs, and CONOPS complexity. The unique challenge for the Army is that all of their critical assets are not only mobile but they are deployed to locations where telematics are not readily available compared to commercial industry operating locations. Finding a solution to deploy telematics that is cost effective and cyber secure is a challenge the Army is looking to solve.
Due to the challenges of telematics implementation, the ability for the U.S. Army to effectively implement predictive maintenance with prognostics for their ground vehicle fleet with a maximum effectiveness relies on the ability to process the sensor data in real time at the edge. Enabling the capability to gather and process vehicle data using on-platform edge devices for prognostics and then transferring data off-platform so additional predictive analytics can be implemented at the Enterprise on-premises sites and/or cloud locations is the approach that will provide the greatest positive impact to the Army.
In order to implement a complete solution to enable PL for Army ground vehicles, an on-platform data processing for prognostics and predictions using edge devices, a deployable telematic solution, and scalable Enterprise analytics is required to serve the entire ground vehicle fleet. One of the major enablers for this objective is the integrated vehicle health management (IVHM) edge system. The IVHM capability can vary from basic warnings and diagnostic faults all the way through embedded prognostics and health predictions and status. With a fully integrated IVHM, machine learning and health algorithms can be run not only in the Enterprise but on the vehicle or asset itself. To date, most of the advanced analytics has occurred in the Enterprise where there is more processing power and access to other data sources. Evolving the analytical capabilities that are present in the Enterprise to where they can be executed on the IVHM is the ultimate goal. To get there the Army will continue to leverage Enterprise analytics and industry lessons learned to advance analytics to prognostics.
Title: Modeling & Simulation (M&S) Tools and Algorithms to Assess Electromagnetic Pulse (EMP) Vulnerability, Survivability, and Mitigation / Shielding
Announcement ID: DAC BAA-022
TPOC: Mr. Robert Hessian, Robert.t.hessian.civ@army.mil & DACBAA@army.mil
DAC Office: Combat System Analysis
Domain: Data & Analysis, Lifecycle Engineering
DAC Foundational Research Competencies: Electronic Warfare (EW) Threat Defense
Army Modernization Priorities: Soldier Lethality, Next Generation Combat Vehicle
Keywords: electromagnetic pulse, EMP, modeling and simulation
Description:
This opportunity focuses on developing and improving M&S tools and algorithms to expand knowledge in two key areas:
i. Assessing mitigation and/or protection factors from EMP effects. This can include additional technologies used to isolate equipment from coupling effects and/or the use of physical or environmental structures (e.g. concrete walls, terrain, surge protectors, other electrical circuit components, etc.) to assess shielding mitigation from EMPs.
ii. Assessing the resulting voltage/current due to coupling between an exposed component (e.g. wire, antenna, etc.) and unexposed component from an EMP exposure.
The development of these M&S tools and algorithms in the EMP area will provide the foundation for developing, integrating, and implementing nuclear threat environments and effects into existing component-level and system-level computational M&S capabilities. Further, these efforts will enable the Army to make faster, more-informed modernization decisions; provide understanding of EMP effects on the battlefield; and enable operational units to maintain strategic and tactical advantages in future nuclear operating and Multi-Domain Operations (MDO) environments.
The objective of this research addresses two aspects related to EMP exposure. The first aspect centers on understanding protective measure performance. More specifically, the research is to investigate materials, data, and available methods that accurately simulate the attenuation and/or mitigation of the effects of an EMP exposure. Moreover, the research would include developing the framework, plans, and executing demonstrations, tests, and analysis/assessments for the development of protection factors (similar to that which exist for exposure to nuclear radiation). This would include developing a test approach to identify potential shielding materials to be analyzed, how the data would be generated, conducting data collection, and performing the evaluation/assessment of the data collected. Development of performance protection factors will consider different material thicknesses, as well as the strength of isolation devices (e.g., surge protection) from the pulses that may be experienced. The result of this research would be data characterizing attenuation based on shielding or coupling protections vice EMP source strength. Ultimately, the objective is to develop shielding performance functions/algorithms for military equipment for use in computational M&S tools in lieu of repeated physical testing for future new items.
The second aspect addresses understanding the coupling performance in exposed elements (e.g., antenna) for a range of EMP source strengths, to include angle of attack (e.g. perpendicular, parallel, etc.). Based on data collected, a coupling assessment should enable the derivation of an algorithm to assess voltage and current from the point of coupling to the associated system. Coupling of components represents a vulnerability to platforms as the associated exposed elements (e.g., antennas, sensors, etc.) are typically not protected from traditional physical shielding mechanisms. The research conducted shall enable the performance data, information, and analysis/assessment to inform design of future mitigation/protection capabilities for associated operating systems, and for assessing the vulnerability of specific components, systems, and/or platforms in M&S environments.
Overall, being able to leverage M&S tools with this complete set of capabilities enables a reduction in future physical testing needs at great savings to Project Managers and Materiel Developers. Improved understanding of EMP effects will improve M&S capabilities in support of battlefield simulations, as well as design development of future platforms. Ultimately, with sufficient data, assessment of design development options via M&S, as opposed to building prototypes for testing to determine performance, will accelerate the design cycle, reduce costs, and support the development of more effective equipment.
Title: Personnel Survivability
Announcement ID: DAC BAA-024
TPOC: Dr. George Steiger, george.e.steiger.civ@army.mil & DACBAA@army.mil
DAC Office: Human Systems Integration
Domain: Data & Analysis
DAC Foundational Research Competencies: Mission Effectiveness Analysis, Human Engineering and Performance
Army Modernization Priorities: Soldier Lethality
Keywords: soldier survivability, SLV, experimentation, analysis, modeling and simulation
Description:
The Personnel Survivability research area concentrates on the advancement of research and analysis to identify, understand, quantify, and model the effects of threat-target interactions on combat personnel, including injury assessment, performance, and operational effectiveness. Advances in research and experiments in the area of personnel survivability are foundational to the identification and development of scientific techniques to: 1) mitigate injury and enhance the survivability of Soldiers facing the wide range of battlefield threats, including ballistic, thermal, toxic substances, and less-than-lethal anti-personnel weapons; 2) enhance lethality of antipersonnel ballistic munitions; and 3) understand the effects of current and future weapons against personnel and protective systems. In addition to Soldier protection, Soldier survivability is assessed in terms of Soldier lethality, situational awareness, and the ability to effectively communicate and operate undetected when needed.
This research focuses on describing a systematic plan of multidisciplinary research that addresses critical questions about the nature of human injury mechanisms and the interactions of the Soldier with enabling technologies that provide protection, lethality, mobility, information, communication and concealment. It addresses adaptive behavior on the battlefield to increase survivability in a hostile environment and the development of methodologies and models to address critical questions surrounding these interactions and implications on Soldier survivability.
DAC will apply these methodologies to identify the benefits and risks associated with new technologies prior to system design and integration supporting capabilities, such as Adaptive Soldier Architecture and trade space analysis of new individual Soldier and squad technologies. DAC will transition our products for use internally and to other DoD users to interactively conduct complex, credible analyses that meet their varied needs as these new technologies mature and are applied to conceptual and developmental Army systems. Using this approach we will extend our subject matter expertise in performing early SLV analysis to “the left” for early SLV (Survivability, Lethality, and Vulnerability) analysis and to “the right” for use in evaluation through the acquisition process, as well as to operational users for mission planning purposes.
This research will also investigate and advance understanding and modeling the Soldier as a system to look at personnel survivability. Personnel survivability is characterized in terms of injury, degraded Soldier performance and capability with and without injury, and risks to long-term quality of life given injury. Because personnel survivability depends on enabling capabilities provided by a number of technologies, personnel survivability is studied in the context of the Soldier as a system, where Soldier lethality and the Soldiers’ ability to sense, communicate, and to operate undetected are also taken into consideration.
The strategic approach includes:
i. Fundamental and applied research projects to understand and quantify human survivability and performance to develop human injury and performance models.
ii. Applied research to understand and characterize next-generation Soldier augmentation systems, protective systems, novel protective materials and future non-lethal and lethal systems that target precision, scalable effects and improved range, the ability to sense and communicate on the battlefield, and to operate undetected.
iii. Development, maintenance, and application of SLV analysis capability that is adaptable, interoperable, and interactive; enables analyses of complex interactions in a mission/capabilities context, exploits emerging computation capabilities and can be used within a collaborative simulation.
iv. Development, application, verification, validation, and transition of tools, techniques and methodologies for testing, analyzing, and evaluating Soldier performance and survivability.
Support to DAC Research Competencies:
Title: Support to DAC Research Competencies
Announcement ID: DAC BAA-026
TPOC: DACBAA@army.mil
DAC Office: Any
Domain: Any
DAC Foundational Research Competencies: Aligns to DAC’s core mission
Army Modernization Priorities: Any
Keywords:
Description:
Under this research topic, DAC will consider whitepapers and proposals that may not directly align to a topic published by a DAC TPOC, but can demonstrate a strong alignment to DAC’s analytic mission. DAC’s research mission is executed within identified applied scientific research competencies that provide the Army foundational expertise and specialized capabilities grounded in scientific excellence and driven by unique Army challenges. DAC is always interested in considering innovative research whitepapers and proposals outside of the published topics on the DAC website that demonstrate a strong alignment to DAC’s foundational research competencies and have the potential to create discovery, innovation, and transition of technologies for Army transformational overmatch.
Social Sharing