Publications
Scalable and Robust Algorithms for Task-Based Coordination From High-Level Specifications (ScRATCHeS)
Summary
Summary
Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We...
Development of a field artifical intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds
Summary
Summary
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in...
A cybersecurity moonshot
Summary
Summary
Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.
Health-informed policy gradients for multi-agent reinforcement learning
Summary
Summary
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then...
Principles for evaluation of AI/ML model performance and robustness, revision 1
Summary
Summary
The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to...
Multilayer microhydraulic actuators with speed and force configurations
Summary
Summary
Electrostatic motors have traditionally required high voltage and provided low torque, leaving them with a vanishingly small portion of the motor application space. The lack of robust electrostatic motors is of particular concern in microsystems because inductive motors do not scale well to small dimensions. Often, microsystem designers have to...
Multimodal representation learning via maximization of local mutual information [e-print]
Summary
Summary
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image...
Learning emergent discrete message communication for cooperative reinforcement learning
Summary
Summary
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase...
Information Aware max-norm Dirichlet networks for predictive uncertainty estimation
Summary
Summary
Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Deep neural networks trained with a conventional method are prone to over-confident predictions. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a...
Ablation analysis to select wearable sensors for classifying standing, walking, and running
Summary
Summary
The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable...