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

Content Provider	IEEE Xplore Digital Library
Author	Fereidunian, A. Zamani, M.A. Boroomand, F. Jamalabadi, H.R. Lesani, H. Lucas, C.
Copyright Year	2010
Description	Author affiliation: Power and Water University of Technology (PWUT), Tehran, Iran (Fereidunian, A.) \|\| CIPCE, ECE, University of Tehran, IRAN (Zamani, M.A.; Boroomand, F.; Jamalabadi, H.R.; Lesani, H.; Lucas, C.)
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.
Starting Page	1534
Ending Page	1539
File Size	354448
Page Count	6
File Format	PDF
ISBN	9781424457939
e-ISBN	9781424457953
DOI	10.1109/MELCON.2010.5476268
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-26
Publisher Place	Malta
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent systems Expert systems Logistics Automation Humans Adaptive systems Engines Databases Fuzzy sets Defense industry
Content Type	Text
Resource Type	Article

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