AALRES: An intelligent expert system for realization of Adaptive Autonomy using Logistic Regression

Abstract

We have introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI) systems, as well as several expert system realizations of that. This study presents an expert system for realization of AA, using logistic regression (LR), referred to as Adaptive Autonomy Logistic Regression Expert System (AALRES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts' judgments. LR is used as the expert system's inference engine. The practical list of PSFs and the judgments of GTEDC's (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implementing AALRES to GTEDC's network are evaluated against the exact predictions of the presented expert system. Evaluations show that AALRES can predict the proper LOA for GTEDC's Utility Management Automation (UMA) system, which change according to changes in PSFs; thus providing an adaptive LOA scheme for UMA.