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We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents.
Nouvelles recherches sur la distribution florale
Paul Jaccard · 1908
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Donald Norman and Dedra Gentner · 1983
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Experimentation in Software Engineering: An Introduction
Claes Wohlin, Per Runeson, Martin Höst, Magnus Ohlsson, Björn Regnell, and Anders Wesslén · 2000
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Transparent queries: investigation users’ mental models of search engines
Jack Muramatsu and Wanda Pratt · 2001
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Q-decomposition for reinforcement learning agents
Stuart Russell and Andrew Zimdars · 2003
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The Sage dictionary of statistics: a practical resource for students in the social sciences
Duncan Cramer and Dennis Howitt · 2004
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Three approaches to qualitative content analysis
Hsiu-Fang Hsieh and Sarah Shannon · 2005
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Everyday expertise: cognitive demands in diabetes self-management
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Saliency and human fixations: state-of-the-art and study of comparison metrics
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Exploring the effects of gaze awareness on multiplayer gameplay
Joshua Newn, Eduardo Velloso, Marcus Carter, and Frank Vetere · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Ruth Fong and Andrea Vedaldi · 2017
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
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How the experts do it: Assessing and explaining agent behaviors in real-time strategy games
Jonathan Dodge, Sean Penney, Claudia Hilderbrand, Andrew Anderson, and Margaret Burnett · 2018
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Explaining deep adaptive programs via reward decomposition
Martin Erwig, Alan Fern, Magesh Murali, and Anurag Koul · 2018
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Sam Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern · 2018
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Metrics for explainable ai: Challenges and prospects
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