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Accelint AI Research

Adaptive Training

Adaptive Training is an advanced instructional method that leverages AI to tailor educational experiences to the needs of individual trainees and teams in real-time. This approach dynamically adjusts the content, pace, and complexity of training materials based on continuous assessments of performance with respect to training objectives, preferences, and individual differences. Accelint’s goal is to optimize training efficiency and effectiveness, ensuring that each learner receives the most appropriate and supportive instruction possible.

Key Features of Accelint’s Adaptive Training Research:

  • Personalization: Customizes training experiences based on individual trainee profiles, including prior knowledge and the application of required skills.
  • Real-Time Feedback: Provides real-time personalized feedback to trainees to help trainees understand mistakes and grasp concepts quickly.
  • Data-Driven Adjustments: Utilizes data analytics to monitor individual trainee and team performance and then adapts the training content dynamically.
  • Engagement Enhancement: Incorporates interactive and engaging elements tailored to maintain trainee interest and motivation. • Scalability: Enables diverse trainee environments and scales to accommodate large numbers of trainees.
  • Generative: Develops AI capabilities to automatically create diverse content (e.g., intelligent agents and scenarios) to enhance training experiences

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Simulation

Simulation-Based Training is an instructional approach that uses XR (virtual/augmented /mixed/live) training environments to replicate real-world military scenarios for training purposes. This method includes synthetic environments (fully virtual settings created using computer-generated imagery and software) and live environments (real-world settings enhanced with virtual elements). Accelint’s goal is to provide trainees with an operationally relevant, controlled, and immersive space where they can practice and hone their skills.

Synthetic training environments include representations of terrain, real-entities, and phenomena (ambient light, weather, and other effects). Accelint’s AI solutions are used to automatically generate realistic scenarios, and create & train non-deterministic, flexible software agents that accurately reflect the behaviors of real entities (friendlies, opposing forces, and neutrals).

Accelint is also conducting research to enhance the efficiency and effectiveness of live training. Based on live training data, Accelint is developing AI solutions to identify live events, behaviors, patterns, and performance trends for use in after action reviews (AARs).

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Autonomy

Autonomous systems are force multipliers that extend DoD reach and add mass while reducing cost and risk to personnel. Developing and deploying them, though, requires advanced algorithms that can support collaborative operations in high threat, denied environments for extended periods. Our autonomous algorithms are designed to engender operator trust, ensure safe and effective operation, and remain robust to adversarial actions. Accelint has been actively researching, developing, and deploying autonomous unmanned systems for 25 years. As an Artificial Intelligence (AI) company We bring the full range of AI tools to address the varied challenges faced by these systems: deep neural networks for high-speed maneuvers and decision-making such as dogfighting, cognitive systems for higher-level decision-making that can reason tactically like a human, explain their choices, integrate with seamlessly with human formations, and maintain trust; and swarm intelligence for distributed control and coordination of large, multi-domain formations of uncrewed autonomous systems. Accelint is also a leader in the development of DevEthOps, our approach to the development and enforcement of ethical, legal, and societal implications (ELSI) in our autonomy algorithms.

Examples: Accelint’s swarm intelligence architecture, SwarmMATE, was used by NAVAIR and SCO to control a swarm of uncrewed aerial systems employing heterogeneous sensors to search, find, and track multiple moving targets in a high threat, communications and GPS denied environment. The swarm was able to coordinate the required sensor geometries to optimize sensor fusion performance for a variety of passive and active EO/IR/RF/radar sensors to achieve track accuracy while maintain required connectivity in the face of jamming. Accelint’s ISOLATE technology developed under DARPA SquadX and DARPA URSA, combines collaborative ground and air autonomy and human-AI teaming, to enable a single operator to monitor a complex environment with hundreds of individuals by fusing data from multiple fixed and robotic sensors and probing actions to detect and assess potential threat actors and actions. ISOLATE successfully completed 19 separate DARPA field events with live actors under challenging conditions. The final experiment adapted the tools for monitoring a prison environment. ISOLATE pioneered the development of our DevEthOps process for designing ELSI aligned human-AI systems.

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Explainable AI

AI can only be a force-multiplier if it is trusted. Accelint is a leader in the research and development of AI technologies, design methods, and evaluation techniques that enable trusted operation.

Accelint is actively researching techniques for high reliability AI reasoning, technologies and displays that explain AI algorithm behavior, and new techniques for the physiological measurement of operators trust.

Example: Accelint’s EpEx is an explanation engine the leverages causal reasoning to explain complex system behavior. EpEx has been applied to medical diagnostics, autonomous system behavior, and adversary behavior recognition in both cyber and Navy domains. EpEx can be easily integrated into new systems and produce explanations in a range of output modes, including human understandable text. Next generation EpEx research will leverage the power of a large language models (LLMs) to bring an expanding breadth of knowledge to explanation problems.

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Data Generation

The DoD needs to be able to deliver updated AI models to warfighters faster than ever before. Achieving that requires new techniques for model generation that aren’t limited by collected training data. Accelint is a leader in developing synthetic data generation techniques across a wide range of data types, from synthetic multi-modal and multi-lingual social media that can replicate a city’s worth of dynamic behavior to synthetic imagery that can emulate a high value target in a wide range of poses, backgrounds, and atmospheric conditions.

Example: Accelint’s ImageZero is an AI/ML toolchain for challenging DoD object, event, and activity detection problems with limited available training data, image fidelity, and computing or networking constraints. ImageZero minimizes the need for collection of training images by using 3D models, real world background images, and physics simulations to create a wide range of high fidelity images for training object detectors. ImageZero research is expanding the range of sensor phenomena that can be modeled and developing a robust ‘Synthetic Data Generation as a Service (SDaaS) pipeline that can be integrated into DoD sensor management pipelines.

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Featured Content

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A Comparison of Behavior Cloning Methods in Developing Interactive Opposing-Force Agents. In The International Florida AI Research Society (FLAIRS) Conference Proceedings (Vol. 36).
Lebanoff, L., Paul, N., Ballinger, C., Sherry, P., Carpenter, G., & Newton, C. (2023, May).
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“Adaptive Learning, Training and Education” in Stephanidis, C. & Salvendy, G. (Eds.) Human Computer Interaction: Foundations and Advances. Routledge
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“Toward the Development of a Cognitive Agent for Wide Area Search,” in National Aerospace and Electronics Conference, Dayton, OH, 2019.
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Automated decision support for collaborative, interactive classification. Artificial Intelligence and Social Computing 72, 1-11.

Publications

Jeff Craighead, PhD.

  • Henry Phillips, Mani Srivastava, Ben Purman, Jeff Craighead, Brian Wang, Julian de Gortari Briseno, Lance Kaplan. “Detection of Complex Events in Synthetic Aerial Sensor Data with NeuroSymbolic Reasoning”. Proceedings of the 2023 Military Sensing Symposia (MSS) Joint Conference – Battlespace Acoustic, Seismic, Magnetic, and Electric-Field Sensing and Signatures Committee (MSS BAMS 2023). November 6-9, 2023.
  • Alyssa Tanaka, Jeffrey Craighead, Glenn Taylor. “The Application of Augmented Reality for Immersive TC3 Training”. Interservice/Industry Training, Simulation and Education Conference. December 2019.
  • Alyssa Tanaka, Jeffrey Craighead, Glenn Taylor, Robert A. Sottilare. “Adaptive Learning Technology for AR Training: Possibilities and Challenges”. 2019 International Conference on Human Computer Interaction. July 2019.

Randolph M. Jones, PhD

  • Jones, R. M., Bixler, R., Marinier, R. P., III, & Moshkina, L. (2023). Automated decision support for collaborative, interactive classification. Artificial Intelligence and Social Computing 72, 1-11.
  • Narayan, N., Ganeriwala, P., Jones, R. M., Matessa, M., Bhattacharyya, S., Davis, J., Purohit, H., Rollini, S. F. (2023). Assuring learning-enabled increasingly autonomous systems. In Proceedings of the 17th Annual IEEE International Systems Conference (SYSCON 2023).
  • Jones, R. M., O’Grady, R., Maymi, F., & Nickels, A. (2019). Cognitive agents for adaptive training in cyber operations. In Proceedings of the 21st International Conference on Human-Computer Interaction (HCII 2019). Orlando, FL.

Daniel Barber, PhD.

  • Gruber, M.E., Hancock, P.A., Barber, D., Wohleber, R., Lyons, J. (2024). The Impact of Transparency on Human-Autonomy Teaming. In the proceedings of the Human Factors and Ergonomics Society Annual Meeting.
  • Atkinson, B.F., Anania, E., Tindall, M., Wohleber, R., Barber, D., Jordan, A.N., Lewis, J.J., Weldon, T. (2023). Applying Human Factors Principles and Analyses to Design an Instructional Display for Dynamic Breathing Threat Training. In the proceedings of the Applied Human Factors and Ergonomics (AHFE).
  • Barber, D., Reinerman-Jones, L., Wohleber, R., Lyons, J. (2022). Multimodal, Adaptable, and Dynamic Human Autonomy Team Relationships. In the proceedings of the Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), Orlando, FL.

Logan Lebanoff, PhD.

  • Lebanoff, L., Newton, C., Hung, V., Atkinson, B., Killilea, J., & Liu, F. (2021, April). Semantic Parsing of Brief and Multi-Intent Natural Language Utterances. In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 255-262). https://aclanthology.org/2021.adaptnlp-1.25.pdf
  • Lebanoff, L., Paul, N., Ballinger, C., Sherry, P., Carpenter, G., & Newton, C. (2023, May). A Comparison of Behavior Cloning Methods in Developing Interactive Opposing-Force Agents. In The International FLAIRS Conference Proceedings (Vol. 36). https://journals.flvc.org/FLAIRS/article/download/133299/137955
  • Koenig, N., Tonidandel, S., Thompson, I., Albritton, B., Koohifar, F., Yankov, G., Speer, A., Hardy, J. H., Gibson, C., Frost, C., Liu, M., McNeney, D., Capman, J., Lowery, S., Kitching, M., Nimbkar, A., Boyce, A., Sun, T., Guo, F., … Newton, C. (2023). Improving measurement and prediction in personnel selection through the application of machine learning. Personnel Psychology, 76, 1061–1123. https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/peps.12608

Lauren (Massey) Glenister

  • Miele, D., Glenister, L., Woods, A. (2023). Developing Methods to Support Social Media Intelligence Analysis. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC 2023). Orlando. Retrieved from https://www.xcdsystem.com/iitsec/proceedings/index.cfm?Year=2023&AbID=121227&CID=1001.
  • Bond , A., Glenister, L., Anania, E., Killilea, J., Wheeler Atkinson, B. F., Stensrud, B., & McSorley, J. (2021). Trust Exercises and Automation Transparency: The Big Fish  Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC 2021). Orlando. Retrieved from https://www.xcdsystem.com/iitsec/proceedings/index.cfm?Year=2021&AbID=97219&CID=862.
  • Massey, L., Smith, R., Whitaker, E.T., Wray, R. (2021). Designing Learning Experiences to Encourage Development of Critical Thinking Skills. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Design and Evaluation. HCII 2021. Lecture Notes in Computer Science(), vol 12792. Springer, Cham. https://doi.org/10.1007/978-3-030-77857-6_5
  • Wray, R., Massey, L., Medina, J., Bolton, A. (2020). Increasing Engagement in a Cyber-Awareness Training Game. In: Schmorrow, D., Fidopiastis, C. (eds) Augmented Cognition. Human Cognition and Behavior. HCII 2020. Lecture Notes in Computer Science(), vol 12197. Springer, Cham. https://doi.org/10.1007/978-3-030-50439-7_10
  • A cognitive skill research framework – Chen, D.-W., Neville, K. J., Massey, L., Burbelo, G. A., Blankenbeckler, P. N., Normand, S., & Uhl, E. (2019). Toward a Definition of Complex Cognitive Skill. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63(1), 1445-1449. https://doi.org/10.1177/1071181319631342
  • Bridgeman, R., Neville, K. J., Massey, L., Krauskopf, C., Mizan, A., Mooney, J., & Schmorrow, D. (2019). Human Factors Electronic Kneeboard Design Guidelines for Military Tactical Aviation. 20th International Symposium on Aviation Psychology, 343-348. https://corescholar.libraries.wright.edu/isap_2019/58
  • Neville, K., Flint, L., Massey, L., Nickels, A., Medina, J., Bolton, A. (2019). Training to Instill a Cyber-Aware Mindset. In: Schmorrow, D., Fidopiastis, C. (eds) Augmented Cognition. HCII 2019. Lecture Notes in Computer Science(), vol 11580. Springer, Cham. https://doi.org/10.1007/978-3-030-22419-6_21
  •  Bond, A., Neville, K., Mercado, J., Massey, L., Wearne, A., Ogreten, S. (2019). Evaluating Training Efficacy and Return on Investment for Augmented Reality: A Theoretical Framework. In: Nazir, S., Teperi, AM., Polak-Sopińska, A. (eds) Advances in Human Factors in Training, Education, and Learning Sciences. AHFE 2018. Advances in Intelligent Systems and Computing, vol 785. Springer, Cham. https://doi.org/10.1007/978-3-319-93882-0_23

Robert Sottilare, PhD.

  • Final Report – Sottilare, R. (2024). Learning & Readiness Intelligent Agent Testbed (LARIAT) BAA Project Final Report for Army Soldier Center. Contract # W912CG-21-C-0002 deliverable.
  • Book Chapter – Sottilare, R. (2024). “Adaptive Learning, Training and Education” in Stephanidis, C. & Salvendy, G. (Eds.) Human Computer Interaction: Foundations and Advances. Routledge (routledge.com/9781032369921).
  • Conference Proceedings – Sottilare, R. A. & Schwarz, J. (Eds.). (2024). Adaptive Instructional Systems: 6th International Conference, AIS 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29–July 4, 2024, Proceedings. Springer Nature.
  • Technical Paper – Sottilare, R. A. (2024, June). Examining the Role of Knowledge Management in Adaptive Military Training Systems. In International Conference on Human-Computer Interaction (pp. 300-313). Cham: Springer Nature Switzerland.
  • Technical Paper – Sottilare, R., Ballinger, C. B., Litvinas, M., McGroarty, C. & Hu, S. (2024, May). Using Genetic Algorithms to Automate Scenario Generation and Enhance the Training Value of Serious Games for Adaptive Instruction. In The International FLAIRS Conference Proceedings (Vol. 37).
  • Litvinas, M., Azevedo, R., Sottilare, R., Ballinger, C., & Hu, S. (2024, May). Tip of the Spear: Developing Predictive Military Planning Tools Using Hidden Markov Models. In The International FLAIRS Conference Proceedings (Vol. 37).
  • Book Chapter – Sottilare, R. A. (2023). Design for Professional Development. Design Recommendations for Intelligent Tutoring Systems: Volume 11-Professional Career Education, 29.
  • Workshop – Sottilare, R., Colby, B. & Jones, R. (2023). Fundamentals of AI for Simulation-Based Training. IITSEC 2023, Orlando, FL, December 2023.
  • Conference Proceedings – Sottilare, R. A., & Schwarz, J. (Eds.). (2023). Adaptive Instructional Systems: 5th International Conference, AIS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings (Vol. 14044). Springer Nature.
  • Technical Paper – Sottilare, R., McGroarty, C., Ballinger, C., & Aris, T. (2023, July). Investigating the Effect of Realistic Agents on Team Learning in Adaptive Simulation-based Training Environments using GIFT. In Proceedings of the 11th Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym11) (p. 31). US Army Combat Capabilities Development Command–Soldier Center.
  • Technical Paper – Sottilare, R., Ballinger, C., & McGroarty, C. (2023, May). Considerations in the Design of Realistic Agents for Serious Games. In The International FLAIRS Conference Proceedings (Vol. 36).
  • Book Chapter – Sottilare, R., & VanLehn, K. (2023). Intelligent Tutoring Systems SWOT. Design Recommendations for Intelligent Tutoring Systems: Volume 10-Strengths, Weaknesses, Opportunities and Threats (SWOT) Analysis of Intelligent Tutoring Systems, 27.
  • Conference Proceedings – Meiselwitz, G., Moallem, A., Zaphiris, P., Ioannou, A., Sottilare, R. A., Schwarz, J., & Fang, X. (Eds.). (2022). HCI International 2022-Late Breaking Papers. Interaction in New Media, Learning and Games: 24th International Conference on Human-Computer Interaction, HCII 2022, Virtual Event, June 26–July 1, 2022, Proceedings (Vol. 13517). Springer Nature.
  • Book Chapter – Czerwinski, E., Goodell, J., Ritter, S., Sottilare, R., Thai, K. P., & Jacobs, D. (2022). Learning engineering uses data (part 1): Instrumentation. In Learning Engineering Toolkit (pp. 153-174). Routledge.
  • Book Chapter – Barrett, M., Czerwinski, E., Goodell, J., Jacobs, D., Ritter, S., Sottilare, R., & Thai, K. P. (2022). Learning engineering uses data (Part 2): Analytics. In Learning Engineering Toolkit (pp. 175-198). Routledge.
  • Book Chapter – Czerwinski, E., Goodell, J., Ritter, S., Sottilare, R., & Thai, K. P. (2022). Data Instrumentation Tools. In Learning Engineering Toolkit (pp. 303-310). Routledge.
  • Conference Proceedings – Sottilare, R. A., & Schwarz, J. (Eds.). (2022). Adaptive Instructional Systems: 4th International Conference, AIS 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26–July 1, 2022, Proceedings (Vol. 13332). Springer Nature.
  • Technical Paper – Sottilare, R. A. (2022, June). AIS Challenges in Evaluating the Selection of Learner Interventions. In International Conference on Human-Computer Interaction (pp. 104-112). Cham: Springer International Publishing.
  • Technical Paper – Sottilare, R., Woods, A., Giranda, N., Bertrand, M., Ortiz, E., & Friedman, B. (2022, May). Facilitating the Integration of Virtual Humans within GIFT. In Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym10) (p. 37).
  • Technical Paper – Brawner, K., Ballinger, C., & Sottilare, R. (2022, May). Evaluating the effectiveness of artificially intelligent agents. Florida AI Research Society.
  • Book Chapter – Sottilare, R. A. (2022) CONSIDERATIONS FOR ADAPTIVE COMPETENCY-BASED SCENARIO DESIGN AND DEVELOPMENT AT SCALE. Design Recommendations for Intelligent Tutoring Systems, Vol 9.
  • Book Chapter – Johnston, J. H., Sottilare, R., Kalaf, M., & Goodwin, G. (2021). Training for team effectiveness under stress. 2022). Design Recommendations for Intelligent Tutoring Systems, Vol 9.
  • Conference Proceedings – Stephanidis, C., Harris, D., Li, W.C., Schmorrow, D.D., Fidopiastis, C.M., Antona, M., Gao, Q., Zhou, J., Zaphiris, P., Ioannou, A. and Sottilare, R.A. (Eds.). (2021). HCI International 2021-Late Breaking Papers: Cognition, Inclusion, Learning, and Culture: 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings (Vol. 13096). Springer Nature.
  • Final Report – Sottilare, R. & Trewhitt, E. (2021). Archimedes Phase II STTR Final Report for NAWCTSD. Contract # N68335-19-C-0004 deliverable.
  • Conference Proceedings – Sottilare, R. A., & Schwarz, J. (Eds.). (2021). Adaptive Instructional Systems. Adaptation Strategies and Methods: Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part II (Vol. 12793). Springer Nature.
  • Conference Proceedings – Sottilare, R. A., & Schwarz, J. (Eds.). (2021). Adaptive Instructional Systems. Design and Evaluation: Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I (Vol. 12792). Springer Nature.
  • Technical Paper – Folsom-Kovarik, J., Sinatra, A. M., & Sottilare, R. A. (2021, July). Automating Team Competency Assessment in Support of Adaptive Dynamic Simulations. In International Conference on Human-Computer Interaction (pp. 199-214). Cham: Springer International Publishing.
  • Technical Paper – Sottilare, R. A., & Brawner, K. W. (2021, July). Scaling Adaptive Instructional System (AIS) Architectures in Low-Adaptive Training Ecosystems. In International Conference on Human-Computer Interaction (pp. 298-310). Cham: Springer International Publishing.
  • Conference Proceedings – Sottilare, R. & Schwarz, J. (Eds.). (2021). Adaptive Instructional Systems: Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24-29, 2021: Proceedings.
  • Technical Paper – Sottilare, R. A., & Brawner, K. (2021). Welcome Virtual Teammates-Modeling the Acceptance of AI-based Entities for Training. In Proceedings of the International AI in Education Conference (pp. 33-39).
  • Book Chapter – Sottilare, R. A., Folsom-Kovarik, J., Cockroft, J. L., & Hampton, A. J. (2020). PERFORMANCE DURING ADAPTIVE INSTRUCTION OF MAINTENANCE TASKS. Design Recommendations for Intelligent Tutoring Systems: Volume 8-Data Visualization, 109.
  • Conference Proceedings – Stephanidis, Constantine, Don Harris, Wen-Chin Li, Dylan D. Schmorrow, Cali M. Fidopiastis, Panayiotis Zaphiris, Andri Ioannou, Xiaowen Fang, Robert A. Sottilare, and Jessica Schwarz (Eds.). (2020) HCI International 2020–Late Breaking Papers: Cognition, Learning and Games: 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings. Vol. 12425. Springer Nature.
  • Technical Paper – Czerwinski, E., Goodell, J., Sottilare, R., & Wagner, E. (2020, August). Learning engineering@ scale. In Proceedings of the Seventh ACM Conference on Learning@Scale (pp. 221-223).
  • Conference Proceedings – Sottilare, R. A., & Schwarz, J. (Eds.). (2020). Adaptive Instructional Systems: Second International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings (Vol. 12214). Springer Nature.
  • Technical Paper – Sottilare, R. (2020, July). Agent-based methods in support of adaptive instructional decisions. In International Conference on Human-Computer Interaction (pp. 164-175). Cham: Springer International Publishing.
  • Technical Paper – Sottilare, R., Knowles, A., & Goodell, J. (2020, July). Representing functional relationships of adaptive instructional systems in a conceptual model. In International Conference on Human-Computer Interaction (pp. 176-186). Cham: Springer International Publishing.
  • Research Report – Sottilare, R., Burke, S., Sinatra, A. M., Johnston, J., Salas, E., & CCDC Soldier Center, Simulation and Training Technology Center Orlando. (2020). Toward a Scientifically Rooted Design Architecture of Team Process and Performance Modeling in Adaptive, Team-Based Intelligent Tutoring Systems. CCDC Soldier Center, Simulation and Training Technology Center Orlando.
  • Best Tutorial – Sottilare, R. & DeFalco, J. (2020). Fundamentals of Adaptive Instructional Systems. IITSEC 2020, Orlando, FL, December 2020.

John Sauter

  •  JA Sauter, K Bixler, S Kitchen, R Chase, RF emitter localization with robotic swarms, In Unmanned Systems Technology XXII, 2020, April (Vol. 11425, p. 114250D), International Society for Optics and Photonics.
  • JA Sauter, K Bixler, Design of unmanned swarm tactics for an urban mission, In Unmanned Systems Technology XXI, 2019, May (Vol. 11021, p. 110210K), International Society for Optics and Photonics.
  •  JA Sauter, K Bixler, SwarmMATE™: a swarm engineering and verification environment, In Unmanned Systems Technology XXI, 2019, May (Vol. 11021, p. 110210N), International Society for Optics and Photonics.

Nick Paul

Elaine Choy

  • Michel, PhD., K., Wohleber, PhD., R., Stensrud, PhD., B., & Choy, E. (2022). Intelligent Data Management Pipeline to Facilitate Human Robot Interaction in Large-Scale Real-Time Environments. What’s My Status? – Best Practices for Self-Led Debriefs.

Victor Hung, PhD

  • Hung, V., Stensrud, B., Singleton, J., Atkinson, B., Entinger, J., and Scheeler, T. (2020). Towards Hypoxia Detection via Automatic Speech Recognition Technology: A Theoretical Technology Framework. SAFE Journal (039), pp. 7-21.
  •  Hung, V., Haley, J., Bridgman, R., Timpko, N., and Wray, R. (2019). Synthesizing Machine-Learning Datasets from Parameterizable Agents Using Constrained Combinatorial Search. Behavior Representation in Modeling and Simulation Conference, Arlington, Virginia.
  • Haley, J., Hung, V., Bridgman, R., Timpko, N., and Wray, R. E. (2018). Low Level Entity State Sequence Mapping to High Level Behavior via a Deep LSTM Model. 20th International Conference on Artificial Intelligence, Las Vegas.
  • IN PROGRESS: Hung, V., Killilea, J., and Giranda, N. (2024). Affordable Access to Advanced Training through Simplified Simulator Development. Interservice/Industry Training, Simulation & Education Conference, Orlando, Florida. (in progress)

JT folsom-Kovarik, PhD

  • Phillips, H., J.T. Folsom-Kovarik, V. Hung, A. Woods, M. Natali, M. Tindall, & B. Atkinson. (2023). Contrasting Predictors of Aviation Training Performance using Machine Learning and Correlations. In the 38th Society of Industrial and Organizational Psychologists Annual Conference (SIOP). April 2023, Boston, MA.
  • Folsom-Kovarik, J.T., Roque, A., & Sinatra, A.M. (2022). Addressing Team Process with Automated Speech Act Assessments. GIFT Symposium 2022.
  • Wray, R.E., Kirk, J. & Folsom-Kovarik, J.T. (2022). Improving Common Ground in Human-Machine Teaming: Dimensions, Gaps, and Priorities. 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022).
  • Folsom-Kovarik, J.T., Sinatra, A, & Sottilare, R.A. (2021). Automating Team Competency Assessment in Support of Adaptive Dynamic Simulations. Human-Computer Interaction International Conference (HCII), July 24-29, LNCS volume 12792.
  • Folsom-Kovarik, J.T., Sieh, J., & Sinatra, A. (2021). Reasoning about Team Roles and Responsibilities for Team Assessment. GIFT Symposium 2021.
  • Folsom-Kovarik, J.T., Sottilare, R.A., & Perez, R.S. (2020). Maintenance Training with Digital Twins and Structured Machine Learning. In the proceedings of the Virtual Interservice / Industry Training, Simulation, and Education Conference (VI/ITSEC) 2020.
  • Haley, J., Carbonara, A., & Folsom-Kovarik, J.T. (2020). Transfer Learning to Create and Understand Modular Content. In the proceedings of the Virtual Interservice / Industry Training, Simulation, and Education Conference (VI/ITSEC) 2020.
  • Folsom-Kovarik, J.T. & Sinatra, A. (2020). Automating Assessment and Feedback for Teamwork to Operationalize Team Functional Resilience. GIFT Symposium 2020.
  • Folsom-Kovarik, J.T., Chen, D.-W., Mostafavi, B., & Freed, M. (2019). Personalization. In J.J. Vogel-Walcutt & S. Schatz, Eds., Modernizing Learning: Building the Future Learning Ecosystem. Washington, DC: Government Publishing Office.
  • Folsom-Kovarik, J.T., Chen, D.-W., Mostafavi, B., & Brawner, K. (2019). Objective measurement of learning content for automated comparison, recommendation, and generation. In the 21st International Conference on Human-Computer Interaction (HCII), Orlando, FL.
  • Folsom-Kovarik, J.T., Mostafavi, B., Sottilare, R.A., Davidson, I., Perez, R., & Walker, P. (2019). Approaches to Enhancing Transfer of Training using Adaptive Instructional Systems. Presented at the 27th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Symposium on Advanced Educational Technologies.
  • Folsom-Kovarik, J.T., Rowe, J., Brawner, K., & Lester, J. (2019). Toward Automated Scenario Generation in GIFT. In Design Recommendations for Intelligent Tutoring Systems: Volume 7 – Self Improving Systems. Anne Sinatra, ed.
  • Dargue, B., Folsom-Kovarik, J.T., & Sanders, J. (2019). Evolving Training Scenarios with Measurable Variance in Learning Impact. In the proceedings of the 21st International Conference on Human-Computer Interaction (HCII), Orlando, FL.
  • Haley, J., Hoehn, R., Folsom-Kovarik, J.T. Wray, R.E., Pazda, R., & Stensrud, B. (2019). Approaches for Deep Learning in Data Sparse Environments. In the proceedings of the Interservice / Industry Training, Simulation, and Education Conference (I/ITSEC) 2019.
  • Hu, X., Cai., Z., Graesser, A., Hampton, A., Cockroft, J., Copland, C., Folsom-Kovarik, J.T., Ramirez-Padron, R., & Tackett, A. (2019). Capturing AIS Behavior using xAPI-like Statements. In the proceedings of the 21st International Conference on Human-Computer Interaction International (HCII) 2019.

Ben Purman

  • Benjamin Purman, Daryn Dever, Bridget Furlong, Matthew Nulle, “Enhancing UAS Sensor Operators’ Performance in Constrained Network Environments,” Presented at Vertical Flight Society Annual Forum 79, 2023
  • Purman, Benjamin, Matthew Nulle, and Bridget Furlong. “Task-based assessment of image compression methods and products.” Unmanned Systems Technology XXIV. Vol. 12124. SPIE, 2022.
  • Martinson, E., B. Purman, and A. Dallas. “Topography Dependent Path Planning.” Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 10-12. Vol. 1. 2021.
  • Russell Craig (Unity), Joe Mercado (Unity), Chris Kawatsu (SoarTech), Ben Purman (SoarTech), “Computer Vision Aided Unexploded Ordnance (UXO) Detection using Synthetic Data,” Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, FL, 2021
  • B. Purman, J. Messing and J. Crossman, “Toward the Development of a Cognitive Agent for Wide Area Search,” in National Aerospace and Electronics Conference, Dayton, OH, 2019.
  • Purman, B., Kawatsu, C., Zhao, A., Gillies, A., Jeffers, M., & Sheridan, P. (2018, May). Real-time inspection of 3D features using sUAS with low-cost sensor suites. In Unmanned Systems Technology XX (Vol. 10640, p. 106400M). International Society for Optics and Photonics.
  • Kawatsu, C., Purman, B., Zhao, A., Gillies, A., Jeffers, M., & Sheridan, P. (2018, May). Automated, near real-time inspection of commercial sUAS imagery using deep learning. In Unmanned Systems Technology XX (Vol. 10640, p. 1064005). International Society for Optics and Photonics.
  • Grigsby, S., Crossman, J., Frederikson, R., Purman, B., and Schmorrow, D. (2017). Intelligent Systems Applied to Workload Monitoring and Task Allocation. Proceedings of the 19th International Society of Aviation Psychology (Dayton)(in press).
  • Grigsby, S., Crossman, J., Purman, B., Frederikson, R., and Schmorrow, D. (2017). Dynamic Task Sharing within Human-UxS Teams: Computational Situation Awareness. In: Schmorrow D.D. & Fidopiastis, C.M. (eds) Augmented Cognition: Enhancing Cognition and Behavior in Complex Environments, Part 2. HCI 2017, Springer (Vancouver) (in press).
  • Taylor, G., Purman, B., Schermerhorn, P., Garcia-Sampedro, G., Hubal, R., Crabtree, K., Rowe, A., Spriggs, S. (2015). Multi-Modal Interaction for UAS Control. Paper presented at the SPIE.DSS, Baltimore, MD.
  • Taylor, G., Purman, B., Schermerhorn, P., Garcia-Sampedro, G., Lanting, M., & Kawatsu, C. (2015). Natural Interaction for Unmanned Systems.” Paper presented at the SPIE.DSS, Baltimore, MD.
  • B. Purman, J. Spencer, J. M. Conk, “Prediction-based registration: An Automated Multi-INT Registration Algorithm,” in Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, Orlando, FL, 2004.

Brice Colby, PhD.

  • Colby, B., Tucker, E., & Siggins, T. (2024). Beyond standalone systems: Creating an ecosystem of adaptive training services. In R. Sottilare & J. Schwarz (Eds.), Adaptive Instructional Systems: Proceedings of the 6th International Conference, AIS 2024, held as part of the 26th HCI International Conference, HCII 2024 (pp. 3-14). Springer.

Le Nguyen