Dissertation/Thesis Abstract

Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems
by Dreany, Harry Hayes, Ph.D., The George Washington University, 2018, 204; 10688677
Abstract (Summary)

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.

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.

Indexing (document details)
Advisor: Roncace, Robert, Fomin, Pavel
Commitee: Etemadi, Amir, Fomin, Pavel, Mazzuchi, Thomas, Roncace, Robert, Sarkani, Shahram
School: The George Washington University
Department: Systems Engineering
School Location: United States -- District of Columbia
Source: DAI-B 79/07(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Engineering, Systems science, Artificial intelligence
Keywords: Artificial intelligence, Cognitive science, Intelligent technologies, System safety
Publication Number: 10688677
ISBN: 9780355631623
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