Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

Sue E. Kase; Michelle Vanni; Justine Caylor; Jeff Hoye

doi:10.1117/12.2263704

3 May 2017 Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

Sue E. Kase, Michelle Vanni, Justine Caylor, Jeff Hoye

Author Affiliations +

Proceedings Volume 10207, Next-Generation Analyst V; 1020705 (2017) https://doi.org/10.1117/12.2263704
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

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 platform.

Conference Presentation

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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 (3 May 2017); https://doi.org/10.1117/12.2263704

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