Uncertainty-aware Artificial Intelligence for More Effective Decision Making

Abstract	This work addresses the determination of uncertainty in AI&ML reasoning when the training data is sparse. Such reasoning should output low uncertainty when the observational sample under test are representative of the training data, but the uncertainty must be high when the test sample is not representative. The characterization of uncertainty is established for low level (i.e., neural networks) and high level (i.e., probabilistic graphical models) reasoning systems.
Authors	Lance Kaplan (ARL) Jin-Hee Cho Murat Sensoy Feng Chen Paul Sullivan (ARL)
Date	Aug-2018
Venue	Army Science & Technology Symposium & Showcase