A Novel Approach to Automated Assessment Generation Using Semantic Extraction
Terry Patten1, Rachel Amey2, Joanne Barnieu3, Clarence Dillon3, Jennifer Harvey3, Sean Shiverick3, Michael Smith3, Steve Hookway1 Paper Session, MODSIM 2023, Norfolk, VA (May 2023). 1 Charles River Analytics 2 U.S. Army Research Institute for the Behavioral and Social Sciences 3 ICF Rapid and reliable individual-level assessments are critical to developing effective and capable Army Soldiers and meeting the […]
Translators as Information Seekers: Strategies and Novel Techniques
Ming Qian HCI International, Virtual (July 2023) Translators search for information to resolve various types of uncertainties they face such as confirming the source of original texts, gaining proper understanding, and verifying whether the selected keywords are spelled correctly, commonly used, or matched properly between source and target languages. Under the constraints of tighter time […]
A Joint, Adaptive, Robust Visualization and Interaction System for AI-Enabled, Symbiotic Cyber-Physical System Design
Ryan Kilgore, David Koelle, Matthew Miller, Amanda Warren, Gabrielle Loeff and Nicholas Alico Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, DC (July 2024). A cyber-physical system (CPS) is a collection of physical and computer components that are integrated with each other to operate a process safely and efficiently. Artificial intelligence […]
Enabling Human-centered Machine Translation Using Concept-based Large Language Model Prompting and Translation Memory
Ming Qian Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, DC (July 2024). This study evaluates a novel human-machine collaborative machine translation workflow, enhanced by Large Language Model features, including pre-editing instructions, interactive concept-based post-editing, and the archiving of concepts in post-editing and translation memories. By implementing GPT-4 prompts for concept-based […]
Growing an Embodied Generative Cognitive Agent
Spencer K. Lynn, Bryan Loyall, James Niehaus Presented at the Association for the Advancement of Artificial Intelligence, AAAI 2023 Fall Symposium Series. (AAAI-23), Arlington, VA, (October 2023). An evolutionary perspective on embodiment puts maintenance of physiology within a functional envelope as the brain’s base goal, with all other goals as refinements. Thus, all goals have […]
Hybrid-AI Approach to Health Monitoring of Vehicle Control System
Kenneth Lu1, Margarita Hiett1, Ernest Vincent Cross1, Michael Reposa1, Aaron Kain2, Erik Davis2 Proceedings of The 70th Annual Reliability & Maintainability Symposium (RAMS®) (January 2024) Advances in Artificial Intelligence and Machine Learning AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes. In this paper, we present novel research that leverages the power […]
CrowdSim: A Generative Model of Crowdsourced Survey Responses
Michael Lepori, Derek Thayer, Sean Guarino, Leonard Eusebi Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, Florida (1 December 2021) Career development relies on an understanding of possible future roles, available training experiences, and current skills. To target training where it will be most effective, trainees and instructors must understand how these elements interact, which […]
Towards a Human-in-the-Loop System for Authoring Game AI using Behavior Languages
Erica Kleinman1, Spencer Lynn2, Bryan Loyall2, Magy Seif El-Nasr3 In Proceedings of the 18th International Conference on the Foundations of Digital Games. Lisbon, Portugal (April 2023) As games get more complicated, Artificially Intelligent (AI) agents need to be better developed to understand and replicate complex, goal-oriented, reactive behaviors. Many existing behavior language approaches do not […]
Cyber Reactive Adversary Framework for Training
Sean Guarino1, William Norsworthy1, David Kelle1, John Steigerwald1, Timothy Ho1, Dorsey Wilkin2 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, Florida (29 November 2023) Networks have become a critical background for military operations as adversaries and hackers become increasingly prolific and proficient at cyber warfare. Despite this, cyber training has remained focused on large-scale exercise […]
Immersive Space Operations Training in Extended Reality
Daniel Stouch, Sean Guarino, Dan Duggan, Susan Latiff, Robert Hyland, Kimberly Brady Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, Florida (29 November 2023) The training of next-generation space operators for both commercial and military capabilities relies largely on analog physical models and PowerPoint lectures. Consequently, new operators do not often fully grasp the fundamentals […]
AI Inference of Team Effectiveness for Training and Operations
Robert Hyland, Kenneth R. Lu, Spencer Lynn, Ph.D, Stephen J. Marotta, James Niehaus, Ph.D., William Norsworthy Jr., Avi Pfeffer, Ph.D., Curtis Q. Wu, Bryan Loyall, Ph.D. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, Florida (29 November 2023) How can we build artificial intelligence (AI) that robustly recognizes how well a team is doing from […]
Democratizing AI for Condition-Based Maintenance using Probabilistic Programming
Kenneth Lu1, Sanja Cvijic1, David Dewhurst1, Joe Gorman1, Rob Hyland1, James Templin2 Proceedings of The 69th Annual Reliability & Maintainability Symposium (RAMS®) (January 2023) Advances in AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes; however, AI/ML solutions themselves inevitably become a maintenance liability, wherein the end users must repeatedly work with […]
Need for AI in Transformer Diagnostics and Prognostics
Sanja Cvijic1, Nidhi Gupta1, Scott Lux2 Proceedings of The 69th Annual Reliability & Maintainability Symposium (RAMS®) (January 2023) Power equipment, such as electrical transformers, undergo performance degradation over time that can prevent them from functioning properly in critical situations or even cause catastrophic failures. Improved diagnostic and prognostic approaches that more accurately determine the reliability […]
Ecological and Coevolutionary Dynamics in Modern Markets Yield Nonstationarity in Market Efficiencies
Van Oort, C., Ring, J.H., Dewhurst, D., Danforth, C. and Tivnan, B. Complexity, Volume 2022, Article ID 7109529 (June 2022) U.S. stock market is one of the largest and most complex marketplaces in the global financial system. Over the past several decades, this market has evolved at multiple structural and temporal scales. New exchanges became […]
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations
Pfeffer, A., Harradon, M., Campolongo, J., and Cvijic, S. arXiv:2110.02325 (October 2021) We introduce Scruff, a new framework for developing AI systems using probabilistic programming. Scruff enables a variety of representations to be included, such as code with stochastic choices, neural networks, differential equations, and constraint systems. These representations are defined implicitly using a set […]
Demonstration of an Explainable AI System Performing Human-Machine Teaming in a Real-Time Strategy Game
Moody, V., Druce, J., and Niehaus, J. Educational Advances in Artificial Intelligence 2022 (EAAI-22) (February 2022) In this paper we demonstrate the use of an Explainable Artificial Intelligence (XAI) system that enables users to team effectively with Artificial Intelligence (AI) agents in a real time strategy (RTS) game. Recent advances in deep reinforcement learning (DRL) […]
Towards Incorporating Artificial Intelligence in the Mission Planning Process
Kane, S., Moody, V, and Harradon, M. HCI International, Washington, DC (June 2021) While there are numerous powerful tools to support Navy mission planning, the mission planning process still remains a hybrid planning activity across human operators and advanced tools. Advances in artificial intelligence (AI) have seen an increase in interest and use in the mission planning environment. Yet traditional […]
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
Druce, J.1, Niehaus, J.2, Moody, V.1, Jensen, D.2, Littman, M.3 arXiv:2106.05506v1 (June 2021) The advances in artificial intelligence enabled by deep learning architectures are undeniable. In several cases, deep neural network driven models have surpassed human level performance in benchmark autonomy tasks. The underlying policies for these agents, however, are not easily interpretable. In fact, […]
The Growing Amplification of Social Media: Measuring Temporal and Social Contagion Dynamics for Over 150 Languages on Twitter for 2009–2020
Alshaabi, T., Dewhurst, D., Minot, J., Arnold, M., Adams, J., Danforth, C., and Dodds, P. EPJ Data Sci. 10, 15 (March 2021) Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. […]
The Sociospatial Factors of Death: Analyzing Effects of Geospatially-distributed Variables in a Bayesian Mortality Model for Hong Kong
Alshaabi, T.1,2,4, Dewhurst, D.1,2,3, Bagrow, J.1,5, Dodds, P.1,2,4, Danforth, C.1,2,5 PLoS ONE 16(3): e0247795 (March 2021) Human mortality is in part a function of multiple socioeconomic factors that differ both spatially and temporally. Adjusting for other covariates, the human lifespan is positively associated with household wealth. However, the extent to which mortality in a geographical […]
SPICEs: Survey Papers as Interactive Cheat-sheet Embeddings
Prabhu, V.1, McAteer, M.2, Teehan, R.3 Rethinking ML Papers – ICLR 2021 Workshop Papers are hard to write. Survey papers are just that much harder. From the authors’ perspective, challenges include the responsibility to not erase out important work being done by (sometimes) adversarially aligned research groups, finding the right semantic clustering to sub-categorize individual contributions, […]
Development of Human-Out-of-the-Loop Participant Recruitment, Data Collection, Data Handling, and Participant Management System
Bracken, B.1, Potoczny-Jones, I.2, Wolcott, J.2, Raffaele, E.2, Woodward, L.2, Gogoel, C.3, Kiourtis, N.3, Schulte, B.3, Arean, P.4, and Farry, M.1 Proceedings of the Human Factors and Ergonomics Society Annual Meeting (February 2021) Most human subjects research requires data collection by contacting local participants who visit a research site. This is costly, time-consuming, and decreases subject retention with each required visit. Additionally, studies require increasingly […]
How the World’s Collective Attention is Being Paid to a Pandemic: COVID-19 Related n-gram Time Series for 24 Languages on Twitter
Alshaabi, T., Arnold, M., Minot, J., Adams, J., Dewhurst, D., Reagan, A., Muhamad, R., Danforth, C., and Dodds, D. PLoS ONE 16(1): e0244476 (January 2021) In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 […]
Structural time series grammar over variable blocks
Dewhurst, D. arXiv:2009.06865v1 (September 2020) A structural time series model additively decomposes into generative, semantically-meaningful components, each of which depends on a vector of parameters. We demonstrate that considering each generative component together with its vector of parameters as a single latent structural time series node can simplify reasoning about collections of structural time series […]
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter
Alshaabi, T.1,2,3, Adams, J.1,2, Arnold, M.1,2, Minot, J.1,2, Dewhurst, D.1,2,4, Reagan, A.5, Danforth, C.1,2,3, and Sheridan Dodds, P.1,2,3 Science Advances (July 2021) In real time, Twitter strongly imprints world events, popular culture, and the day-to-day, recording an ever-growing compendium of language change. Vitally, and absent from many standard corpora such as books and news archives, […]
Augmented Riding: Multimodal Applications of AR, VR, and MR to Enhance Safety for Motorcyclists and Bicyclists
Kingsley, C., Flowers, A., Negri, A., Duggan, D., Bird, L., and Jenkins, M. Proceedings of the 22nd International Conference on Human-Computer Interaction (HCII 2020), Virtual (July 2020). Operating two-wheeled vehicles in four-wheel-dominant environments presents unique challenges and hazards to riders, requiring additional rider attention and resulting in an increased inherent risk. Crashes involving motorcycles or bicycles […]
Continuous Cognitive Workload Assessment and Combined Metrics of Performance in the Multi-Attribute Task Battery
Bracken, B.1, Endsley, M.2, Tobyne, S.1, Leather, C.1, and Farry, M.1 Presented at the 11th International Conference on Applied Human Factors and Ergonomics Conference (AHFE), San Diego, CA (July 2020) Neurophysiological correlates of cognitive workload (e.g., changes in brain blood oxygenation) are detectable with minimally-invasive wearable sensors including functional near infrared spectroscopy (fNIRS). When cognitive […]
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
Druce, J., Harradon, M., Tittle, J. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada We consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted. We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation: […]
A Toolkit to Help Integrate Humans with Virtual Environments, Intelligent Simulations (FSMs), and Artificial Cognitive Systems (NPCs)
Kingsley, C. and Jenkins, M. Proceedings of the 2019 Intelligent Human Systems Integration (IHSI) International Conference, San Diego, CA (February 2019) Charles River Analytics is developing an open-source, Unity-based Extended Reality (XR) SDK for development of immersive simulations, currently focused on medical simulations. Our work-to-date has focused on integrating humans into virtual environments through enhanced […]
Bot Detection: Will Focusing on Recall Cause Overall Performance Deterioration?
Nazer, T.1, Davis, M.1, Karami, M.1, Akoglu, L.2, Koelle, D.3, and Liu, H.1 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Washington, DC (July 2019) Social bots are an effective tool in the arsenal of malicious actors who manipulate discussions on social media. Bots help spread misinformation, promote political […]
Bot Detection: Will Focusing on Recall Cause Overall Performance Deterioration?
Nazer,T.H.1, Davis, M.1, Karami, M1, Akoglu, L.2, Koelle, D.3, and Liu, H.1 Presented at the 2019 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Washington, DC (July 2019) Social bots are an effective tool in the arsenal of malicious actors who manipulate discussions on social media. Bots […]
Probabilistic Programming: Past, Present, and Future
Pfeffer, A. Invited keynote at the 22nd International Conference on Information Fusion (Fusion 2019), Ottawa, Canada (July 2019) Overview: What is Probabilistic Programming? Probabilistic Programming in Action Probabilistic Programming Inference Algorithms Probabilistic Programming for Long-Lived AI Systems Download Slides For More Information To learn more, contact Avi Pfeffer. (Please include your name, address, organization, and […]
Software Adaptation for an Unmanned Undersea Vehicle
Pfeffer, A.1, Wu, C.1, Fry, G.1, Lu, K.1, Marotta, S.1, Reposa, M.1, Shi, Y.2, Satish Kumar, T.2, Knoblock, C.2, Parker, D.3, Muhammad, I.3, and Novakovic, C.3 IEEE Software, Vol. 36, No. 2 (March/April 2019) Unmanned undersea vehicles (UUVs) are designed to carry out challenging missions in changing environments. To maximize their effectiveness, these vehicles should adapt to system failures […]
Combining Data‐Driven and Theory‐Driven Models for Causality Analysis in Sociocultural Systems
Sliva, A.1, Neal Reilly, S.1, Blumstein, D.1, and Pierce, G.2 In Social-Behavioral Modeling for Complex Systems, Paul K. Davis, Angela O’Mahony, and Jonathan Pfautz (Eds) To better understand and describe sociocultural systems, it is critical that we can create, analyze, and validate social, political, and economic models that capture causal and predictive dynamics. Causality is, however, notoriously difficult […]
Privacy Preserving Neural Network Inference on Encrypted Data with GPUs
Takabi, H.1, Podschwadt, R.2, Druce, J.3, Wu, C.3, and Procopio, K3. Presented at Privacy in Machine Learning, NeurIPS 2019 Workshop, Vancover, BC (December 2019) Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered. MLaaS is essentially a set of services that provides machine […]
Physiological Indices of Challenge and Threat: A Data-Driven Investigation of Autonomic Nervous System Reactivity During an Active Coping Stressor Task
Wormwood, J.1, Khan, Z.2, Siegel, E.3, Lynn, S.4, Dy, J.2, Barrett, K.2, and Quigley, K2. Psychophysiology (August 2019) We utilized a data‐driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological measures. Results revealed that patterns of […]
Application of the DeepSense Deep Learning Framework to Determination of Activity Context from Smartphone Data
Bracken, B., Manjunath, S., German, S., Monnier, C., and Farry, M. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Volume 63, Issue 1, Pages 792-796 (November 2019) Current methods of assessing health are infrequent, costly, and require advanced medical equipment. 92% of US adults carry mobile phones, and 77% carry smartphones with advanced sensors (Smith, 2017). […]
Spatial Degradation of Color Discrimination in Augmented Reality
Flowers, B. A., Wollocko, A., Kingsley, C., Thiry, E., and Jenkins, M. 10th International Conference on Applied Human Factors and Ergonomics (AHFE 2019), Washington D.C. (July 2019) Stereoscopic Augmented Reality displays are known to degrade the color perception of users. Using a spatially-aware color matching task, we performed a repeated trial study in which participants […]
Using Sociocultural Data from Online Gaming and Game Communities
Guarino, S., Eusebi, L., Bracken, B., and Jenkins, M. In Social-Behavioral Modeling for Complex Systems, Paul K. Davis, Angela O’Mahony, and Jonathan Pfautz (Eds) (April 2019) This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, […]
Characterizing the Limits of Visual Field Augmentation in Augmented Reality: Psychophysical Foundations for Design and Mode Transition Acclimation Strategies
Jenkins, M., Kingsley, C., and Flowers, B.A. Panel Presentation at the 22nd Annual Applied Ergonomics Conference, New Orleans, LA (March 2019) The impact of augmented reality displays on the human perceptual system will vary across headsets. Because of this, the display used for any solution should be selected with an awareness of the interaction between the […]
Automation Support using non-Invasive Measures of Operator Vocalization (ASIMOV)
Elkin-Frankston, S.1, Leather, C.1, Bracken, B.1, and van Mersbergen, M.2 To be presented at the 11th International Conference on Voice Physiology and Biomechanics (ICVPB 2018), East Lansing, MI (July 2018). Heightened states of arousal can be investigated using electroglottography (EGG) contact quotient, as well as other measures of vocalization, such as surface laryngeal electromyography (sEMG), […]
A Predicative Processing Model of Categorical Perception
Lynn, S. Presented at the 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Washington DC, USA (July 2018) Prior knowledge influences perception, as evidenced by categorical perception phenomena, in which expectations create psychometric distortions of perceptual space. These distortions are nonetheless associated with categorization accuracy. […]
Structured Factored Inference for Probabilistic Programming
Pfeffer, A., Ruttenberg, B., Kretschmer, W., and O’Connor, A. Presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands (April 2018) Probabilistic reasoning on complex real-world models is computationally challenging. Inference algorithms have been developed that work well on specific models or on parts of general models, but they […]
Scruff: A Deep Probabilistic Cognitive Architecture
Pfeffer, A. Invited talk at the Association for the Advancement of Artificial Intelligence’s Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA (February 2018). Probabilistic programming is able to build rich models of systems that combine prior knowledge with the ability to learn from data. One of the reasons for the success of deep […]
The Support Method of Computing Expectations
Pfeffer, A. Extended abstract for the Probabilistic Program Semantics Workshop associated with the Principles of Programming Languages (POPL) conference, Los Angeles, CA (January 2018). A standard method for computing the expectation of a function on a probabilistic program is to sample values from the generative model and approximate the integral. An alternative approach is to […]
Using Reinforcement Learning for Probabilistic Program Inference
Pfeffer, A. Extended abstract for the Probabilistic Program Semantics Workshop associated with the Principles of Programming Languages (POPL) conference, Los Angeles, CA (January 2018). Inference in probabilistic programming often involves choosing between different methods. For example, one could use different algorithms to compute a conditional probability, or one could sample variables in different orders. Researchers […]
Predict and Analyze Novel and Emerging Diseases Enabled by Models of Infection Conditions
Prue, B.1, Voge, J.1, Mara, J.1, Wollocko, A.1, and Mekaru, S.2 Poster presentation at the International Conference of Human-Computer Interaction (HCII 2018), Las Vegas, NV (July 2018) Incidents of infectious disease pose serious threats to armed forces worldwide, risking the success of critical operations and the deaths of Warfighters. Force medical personnel require support to […]
An Integrated Threat-based Approach to Intuitive Space Battle Management Understanding
Stouch, D., Jenkins, M., McCaffrey, J., and Catto, G. Air Force Research Laboratory Space Situational Awareness Conference, Kihei, HI (September 2018) To effectively conduct battle management command and control (BMC2) in space, operators need to understand the space environment well beyond what is required for traffic management that predicts potential conjunctions. Operators need to know […]
Contamination and Lasting Effects Analysis for Negative Substances and Elements
Voge, J., Jacobs, P., Negri, A., and Farry, M. Poster presented at the International Conference on Applied Human Factors and Ergonomics (AHFE), Orlando, FL (July 2018). Military training facility managers are tasked with understanding and responding to the long-term effects of contaminant releases. To mitigate disastrous environmental damage to ecosystems, military training facility managers must […]
Leveraging Systemic Functional Grammars for Script Analysis and Understanding Human Behavior
Sliva, A., Call, C., and Patten, T. Presented at the 45th International Systemic Functional Congress (ISFC 2018), Boston, MA (July 2018). In sociolinguistics, it is desirable to understand not only social-functional aspects of language, but also the broader social and behavioral landscape. In psychology, script theory posits that human behavior follows discernable patterns, or “scripts,” […]
Predict and Analyze Novel and Emerging Diseases Enabled by Models of Infection Conditions
Prue, B.1, Voge, J.1, Mara, J.1, Wollocko, A.1, and Mekaru, S.2 Poster presentation at the International Conference of Human-Computer Interaction (HCII 2018), Las Vegas, NV (July 2018) Incidents of infectious disease pose serious threats to armed forces worldwide, risking the success of critical operations and the deaths of Warfighters. Force medical personnel require support to […]
Interfacing Systemic Functional Grammars with Frame Semantics
Dohmann, J.1 2, Patten, T2., Campolongo, J.2 Presented at the 45th International Systemic Functional Congress (ISFC 2018), Boston, MA (July 2018). As computational applications play a greater role in society, Systemic Functional Linguistics can play an important role in producing effective interactions between those applications and the people who use them. Significant progress has been […]
Scruff: A Deep Probabilistic Cognitive Architecture for Predictive Processing
Pfeffer, A. and Lynn, S. Biologically Inspired Cognitive Architectures 2018 The theory of predictive processing encompasses several elements that make it attractive as the underlying computational approach for a cognitive architecture. We introduce a new cognitive architecture, Scruff, capable of implementing predictive processing models by incorporating key properties of neural networks into the Bayesian probabilistic […]
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations
Harradon, M., Druce, J., and Ruttenberg, B. arXiv:1802.00541v1 (February 2018) Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained […]
Complex Causality: Computational Formalisms, Mental Models, and Objective Truth
Dalal, M., Sliva, A., and Blumstein, D. Presented at the 7th International Conference on Cross-Cultural Decision Making at the International Conference on Applied Human Factors and Ergonomics, Los Angeles, CA (July 2017). There is a broad consensus that understanding causality is important for comprehending the world and making decisions. However, causality is notoriously difficult to understand […]
Testing the Usability of a Decision-Support System for Increasing Environmental Awareness
Danczyk, J., Jacobs, P., Montgomery, O., Jenkins, M., and Farry, M. Presented at the 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Savannah, GA (March 2017) Military facility managers must track repeated contaminant release that occurs from scheduled training exercises to mitigate the effects of those releases before negative effects occur. Training […]
Automatic Differentiation Equipped Variable Elimination for Sensitivity Analysis on Probabilistic Inference Queries
Druce, J.1, Ruttenberg, B.1, Blumstein, D.1, and Scofield, D.2 Presented at the Conference on Neural Information Processing Systems (NIPS), Long Beach, CA (December 2017) Probabilistic Models are a natural framework for describing the stochastic relationships between variables in a system to perform inference tasks, such as estimating the probability of a specific set of conditions or […]
Designing a Pragmatic Graphical Grammar
Eusebi, L., and Guarino, S. Presented at the 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Savannah, GA (March 2017) Modern adversaries have become more proficient in conducting cyberattacks against our military’s command and control (C2) infrastructure. To maintain security against these threats, operators perform a range of high-fidelity security assessments of […]
Probabilistic Model-Based Programming Techniques for Prediction, Analysis, and Control (PROMPT)
Harrison, S., Takata, G., Wu, C., and Pfeffer, A. Presented at the 15th Annual Conference on Systems Engineering Research Disciplinary Convergence: Implications for Systems Engineering Research, Redondo Beach, CA (March 2017) Model-based systems engineering (MBSE) frameworks such as SysML provide declarative descriptions of the structure, processes, functions, and context of a system. However, these frameworks do not […]
Scratchpad: Lightweight Data Capture Tools to Support Mission Planning
Kane, S., von Kelsch, E., Muller, C., and Hogan, C. Presented at the 19th International Conference on Human-Computer Interaction (HCII 2017), Vancouver, Canada (July 2017) Powerful tools are available to support the mission planning process. Although these tools provide support to multiple components, they do not support the dynamic nature of the process over time. Pilots […]
