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Connection Science Volume 27, 2015 - Issue 3: Adaptive Learning Agents, Part 3
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Articles

A reinforcement learning model of joy, distress, hope and fear

Joost BroekensDepartment of Intelligent Systems, Interactive Intelligence Group, Delft University of Technology, 4 Mekelweg, Delft, The NetherlandsCorrespondence[email protected]
,
Elmer JacobsDepartment of Intelligent Systems, Interactive Intelligence Group, Delft University of Technology, 4 Mekelweg, Delft, The Netherlands
&
Catholijn M. JonkerDepartment of Intelligent Systems, Interactive Intelligence Group, Delft University of Technology, 4 Mekelweg, Delft, The Netherlands
Pages 215-233 | Received 05 Sep 2014, Accepted 25 Feb 2015, Published online: 23 Apr 2015

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Li Hai-Dong, Yang Ya-Juan & Lei Lu. (2022) In the context of COVID-19: the impact of employees’ risk perception on work engagement. Connection Science 34:1, pages 1367-1383.
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Casper Hesp, Bram T. Heerebout & R. Hans Phaf. (2021) Evolutionary computation for bottom-up hypothesis generation on emotion and communication. Connection Science 33:2, pages 296-320.
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Isaac R. Galatzer-Levy, Kelly V. Ruggles & Zhe Chen. (2018) Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience. Chronic Stress 2, pages 247054701774755.
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Abhishek Tripathi, D. G. Manasa, K. Rakshitha, T. S. Ashwin & G. Ram Mohan Reddy. 2018. Recent Findings in Intelligent Computing Techniques. Recent Findings in Intelligent Computing Techniques 507 517 .
Harald Stromfelt, Yue Zhang & Bjorn W. Schuller. (2017) Emotion-augmented machine learning: Overview of an emerging domain. Emotion-augmented machine learning: Overview of an emerging domain.
Robert Lowe, Emilia Barakova, Erik Billing & Joost Broekens. (2016) Grounding emotions in robots – An introduction to the special issue. Adaptive Behavior 24:5, pages 263-266.
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Noor Shaker. (2016) Intrinsically motivated reinforcement learning: A promising framework for procedural content generation. Intrinsically motivated reinforcement learning: A promising framework for procedural content generation.
Robert Lowe & Kiril Kiryazov. 2014. Emotion Modeling. Emotion Modeling 76 98 .

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