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dc.contributor.authorSheh, Raymond
dc.date.accessioned2018-05-18T08:00:27Z
dc.date.available2018-05-18T08:00:27Z
dc.date.created2018-05-18T00:23:07Z
dc.date.issued2017
dc.identifier.citationSheh, R. 2017. Different XAI for different HRI, The Association for the Advancement of Artificial Intelligence 2017 Fall Symposium Series (AAAI 2017), pp. 114-117.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/67997
dc.description.abstract

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Artificial Intelligence (AI) has become more widespread in critical decision making at all levels of robotics, along with demands that the agent also explain to us humans why they do what they do. This has driven renewed interest in Explainable Artificial Intelligence (XAI). Much work exists on the Human-Robot Interaction (HRI) challenges of creating and presenting explanations to different human users in different applications but matching these up with AI and Machine Learning (ML) techniques that can provide the underlying explanatory information can still be a challenge. In this short paper, we present a categorisation of explanations that communicate the XAI requirements of various users and applications, and the XAI capabilities of various underlying AI and ML techniques.

dc.titleDifferent XAI for different HRI
dc.typeConference Paper
dcterms.source.volumeFS-17-01 - FS-17-05
dcterms.source.startPage114
dcterms.source.endPage117
dcterms.source.titleAAAI Fall Symposium - Technical Report
dcterms.source.seriesAAAI Fall Symposium - Technical Report
dcterms.source.isbn9781577357940
dcterms.source.conferenceThe Association for the Advancement of Artificial Intelligence 2017 Fall Symposium Series (AAAI 2017)
dcterms.source.placeUSA
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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