The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data
collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical
operational environment. These types of environments are characteristic of intelligence workflow processes conducted
during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic
overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of
crowds to model the interaction of the information consumer and the information required to solve a problem at different
levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems
(DSS) research represent the experimental conditions in this online single-player against-the-clock game where the
player, acting in the role of an intelligence analyst, is tasked with a Commander’s Critical Information Requirement
(CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks
(annotation, relation identification, and link diagram formation) with the assistance of ‘HAMIE the robot’ who offers
varying levels of information understanding dependent on question complexity. We provide preliminary results from a
pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research
Sue E. Kase, Michelle Vanni, Justine Caylor, and Jeff Hoye, "Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving," Proc. SPIE 10207, Next-Generation Analyst V, 1020705 (Presented at SPIE Defense + Security: April 10, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263704.
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