Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States
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 t...
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| Format: | Others |
| Language: | English |
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Florida Atlantic University
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| Online Access: | http://purl.flvc.org/fau/fd/FA00004764 http://purl.flvc.org/fau/fd/FA00004764 |
| 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. === Includes bibliography. === Thesis (M.S.)--Florida Atlantic University, 2016. === FAU Electronic Theses and Dissertations Collection |
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