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dc.contributor.authorFeigh, Karen
dc.date.accessioned2019-02-15T21:10:52Z
dc.date.available2019-02-15T21:10:52Z
dc.date.issued2019-02-06
dc.identifier.urihttp://hdl.handle.net/1853/60909
dc.descriptionPresented on February 6, 2019 at 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Room 1116-1118, Georgia Tech.en_US
dc.descriptionKaren Feigh is an associate professor in Georgia Tech's Daniel Guggenheim School of Aerospace Engineering. She holds a B.S. in aerospace engineering from Georgia Tech, an MPhil in aeronautics from Cranfield University, UK, and a Ph.D. in industrial and systems engineering from Georgia Tech. Feigh has previously worked on fast-time air traffic simulation, conducted ethnographic studies of airline and fractional ownership operation control centers, and designed expert systems for air traffic control towers and NextGen concepts. She is also experienced in conducting human-in-the-loop experiments for concept validation.en_US
dc.descriptionRuntime: 43:57 minutesen_US
dc.description.abstractA goal of interactive machine learning (IML) is to create robots or intelligent agents that can be easily taught how to perform tasks by individuals with no specialized training. To achieve that goal, researchers and designers must understand how certain design decisions impact the human’s experience of teaching the agent, such as influencing the agent’s perceived intelligence. We posit that the type of feedback a robot can learn from effects the perceived intelligence of the robot, similar to its physical appearance. This talk will discuss different methods of natural language instruction including critique and action advice. We conducted multiple human-in-the-loop experiments, in which people trained agents with different teaching methods but, unknown to each participant, the same underlying machine learning algorithm. The results show that the mechanism of teaching has an impact on a human teacher’s perception of the agent, including feelings of frustration, perceptions of intelligence, and performance, while only minimally impacting the agent’s performance.en_US
dc.format.extent43:57 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIRIM Seminar Seriesen_US
dc.subjectHuman robot interactionen_US
dc.titleHuman Teacher’s Perception of Teaching Methods for Machine Learning Algorithmsen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Aerospace Engineeringen_US


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  • IRIM Seminar Series [112]
    Each semester a core seminar series is announced featuring guest speakers from around the world and from varying backgrounds in robotics.

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