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Safety Engineering of Computational ...

ProQuest Information and Learning Co.

Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems./
Author:	Dreany, Harry Hayes.
Description:	1 online resource (204 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355631623

Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems.
Dreany, Harry Hayes.

Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems. - 1 online resource (204 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2018.

Includes bibliographical references

This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355631623Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems.
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100 1 $a Dreany, Harry Hayes. $3 1190702
245 1 0 $a Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems.
264 0 $c 2018
300 $a 1 online resource (204 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Advisers: Robert Roncace; Pavel Fomin.
502 $a Thesis (Ph.D.)--The George Washington University, 2018.
504 $a Includes bibliographical references
520 $a This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms.
520 $a Cognitive technology is currently simulated within safety-critical systems to highlight variables of interest, interface with intelligent technologies, and provide an environment that improves the system's cognitive performance. In this study, the IDSM is being applied to an actual safety-critical system, an unmanned surface vehicle (USV) with embedded artificial intelligence (AI) software. The USV's safety performance is being researched in a simulated and a real-world, maritime based environment. The objective is to build a dynamically changing model to evaluate a cognitive architecture's ability to ensure safe performance of an intelligent safety-critical system. The IDSM does this by finding a set of key safety performance parameters that can be critiqued via safety measurements, mechanisms, and methodologies. The uniqueness of this research lies in bounding the decision-making associated with the cognitive architecture's key safety parameters (KSPs). Other real-time applications (RTAs) that would benefit from advancing cognitive science associated with safety are unmanned platforms, transportation technologies, and service robotics. Results will provide cognitive science researchers with a reference for the safety engineering of artificially intelligent safety-critical systems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Systems science. $3 1148479
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0790
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Systems Engineering. $3 1148622
773 0 $t Dissertation Abstracts International $g 79-07B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10688677 $z click for full text (PQDT)

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