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Deep Learning, transparency and trust in Human Robot Teamwork

Lewis, Michael and Li, Huao and Sycara, Katia (2020) Deep Learning, transparency and trust in Human Robot Teamwork. In: Trust in Human-Robot Interaction. Elsevier, New York, NY. ISBN UNSPECIFIED (In Press)

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Abstract

For Autonomous AI systems to be accepted and trusted, the users should be able to understand the reasoning process of the system (i.e., the system should be transparent). Robotics presents unique programming difficulties in that systems need to map from complicated sensor inputs such as camera feeds and laser scans to outputs such as joint angles and velocities. Advances in Deep Neural Networks are now making it possible to replace laborious handcrafted features and control code by learning control policies directly from high dimensional sensor inputs. Because Atari games, where these capabilities were first demonstrated, replicate the robotics problem they are ideal for investigating how humans might come to understand and interact with agents who have not been explicitly programmed. We present computational and human results for making DRLN more transparent using object saliency visualizations of internal states and test the effectiveness of expressing saliency through teleological verbal explanations.


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Details

Item Type: Book Section
Status: In Press
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lewis, Michaelcmlewis@pitt.educmlewis0000-0002-1013-9482
Li, Huaohuao.roman.li@gmail.comhuao.li
Sycara, Katiakatia@cs.cmu.edu
Date: 2020
Date Type: Publication
Publisher: Elsevier
Place of Publication: New York, NY
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Title of Book: Trust in Human-Robot Interaction
Editors:
EditorsEmailPitt UsernameORCID
Nam, ChangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Lyons, JosephUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date Deposited: 27 May 2020 14:30
Last Modified: 27 May 2020 14:30
URI: http://d-scholarship.pitt.edu/id/eprint/39097

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