Real time machine learning and decision-making with hardware expert systems and gradient descent trained expert systems

Abstract

Three previously proposed machine learning techniques (a technique based on applying gradient descent training to a fractional value expert system, the fractional value expert system technique’s real time hardware implementation, and a Boolean expert system hardware implementation) are evaluated for application to defense-relevant real time processing challenges. These techniques are defensible, meaning that their decisions are constrained by logical pathways that can be reviewed by a human prior to a decision being made and acted upon by the system (as opposed to simply explained afterwards). These techniques and several conventional techniques are evaluated under multiple defense-relevant real time processing scenarios.