| Title | Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Cobb, Adam D., Jalaian, Brian A., Bastian, Nathaniel D., Russell, Stephen |
| Conference Name | 2021 Winter Simulation Conference (WSC) |
| Date Published | dec |
| Keywords | Costs, decision making, human factors, iobt, machine learning, Neural networks, pubcrawl, resilience, Resiliency, Scalability, Training, Transforms, Uncertainty |
| Abstract | The Internet of Battlefield Things (IoBT) is a dynamically composed network of intelligent sensors and actuators that operate as a command and control, communications, computers, and intelligence complex-system with the aim to enable multi-domain operations. The use of artificial intelligence can help transform the IoBT data into actionable insight to create information and decision advantage on the battlefield. In this work, we focus on how accounting for uncertainty in IoBT systems can result in more robust and safer systems. Human trust in these systems requires the ability to understand and interpret how machines make decisions. Most real-world applications currently use deterministic machine learning techniques that cannot incorporate uncertainty. In this work, we focus on the machine learning task of classifying vehicles from their audio recordings, comparing deterministic convolutional neural networks (CNNs) with Bayesian CNNs to show that correctly estimating the uncertainty can help lead to robust decision-making in IoBT. |
| DOI | 10.1109/WSC52266.2021.9715532 |
| Citation Key | cobb_robust_2021 |
Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks