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Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States
- Date Issued:
- 2016
- Summary:
- A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.
Title: | Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States. |
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Name(s): |
Lloyd, Eric, author Huang, Shihong, Thesis advisor Florida Atlantic University, Degree grantor College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Created: | 2016 | |
Date Issued: | 2016 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 159 p. | |
Language(s): | English | |
Summary: | A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions. | |
Identifier: | FA00004764 (IID) | |
Degree granted: | Thesis (M.S.)--Florida Atlantic University, 2016. | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): | Includes bibliography. | |
Subject(s): |
Cognitive neuroscience. Neural networks (Computer science) Pattern recognition systems. Artificial intelligence. Self-organizing systems. Human-computer interaction. Human information processing. |
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Held by: | Florida Atlantic University Libraries | |
Sublocation: | Digital Library | |
Links: | http://purl.flvc.org/fau/fd/FA00004764 | |
Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00004764 | |
Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |