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ZENODO
Article . Conference object . Other literature type . 2019
License: CC BY NC ND
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . Conference object . Other literature type . 2019
License: CC BY NC ND
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Machine Coaching

Authors: Michael, Loizos;

Machine Coaching

Abstract

This position paper puts forward machine coaching as a form of interactive machine learning that emphasizes the requirement for humans and machines to externalize their internal reasoning process in a manner that is understandable, at least at a basic level, by the other party. We posit that this mutual understanding leads to a computationally and cognitively lighter interaction, supports the run-time personalization of machines even by non-technically-savvy humans, makes any machine biases explicit and the process of their acquisition transparent, and facilitates the development of AI systems that can, by design, explain and be explained to. Backed by psychological theories of human reasoning and recent technical work, this paper adopts the working hypothesis that argumentation over symbolic rulebased knowledge offers a reasonable common language and semantics that machines and humans can utilize when interacting through machine coaching.

This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and under Grant Agreement No 823783 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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