Difference between revisions of "Rolletetal2017"
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{{BibEntry | {{BibEntry | ||
− | | | + | |BibType=INCOLLECTION |
− | | | + | |Author(s)=Nicolas Rollet; Varun Jain; Christian Licoppe; Laurence Devillers; |
|Title=Towards Interactional Symbiosis: Epistemic Balance and Co-presence in a Quantified Self Experiment | |Title=Towards Interactional Symbiosis: Epistemic Balance and Co-presence in a Quantified Self Experiment | ||
− | |||
|Tag(s)=EMCA; conversation analysis; epistemics; human-robot interaction; preference; quantified self; robots | |Tag(s)=EMCA; conversation analysis; epistemics; human-robot interaction; preference; quantified self; robots | ||
+ | |Key=Rolletetal2017 | ||
+ | |Year=2017 | ||
|Booktitle=Lecture Notes in Computer Science | |Booktitle=Lecture Notes in Computer Science | ||
− | |||
− | |||
|Pages=143–154 | |Pages=143–154 | ||
|URL=http://link.springer.com/10.1007/978-3-319-57753-1\_13 | |URL=http://link.springer.com/10.1007/978-3-319-57753-1\_13 |
Revision as of 05:42, 26 September 2017
Rolletetal2017 | |
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BibType | INCOLLECTION |
Key | Rolletetal2017 |
Author(s) | Nicolas Rollet, Varun Jain, Christian Licoppe, Laurence Devillers |
Title | Towards Interactional Symbiosis: Epistemic Balance and Co-presence in a Quantified Self Experiment |
Editor(s) | |
Tag(s) | EMCA, conversation analysis, epistemics, human-robot interaction, preference, quantified self, robots |
Publisher | |
Year | 2017 |
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Pages | 143–154 |
URL | Link |
DOI | 10.1007/978-3-319-57753-1_13 |
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Edition | |
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Book title | Lecture Notes in Computer Science |
Chapter |
Abstract
In the frame of an experiment dealing with quantified-self and re- flexivity, we collected audio-video data that provide us with material to discuss the ways in which the participants would work out social synergy through co- presence management and epistemic balance – accounting for their orientation towards the familiar symbiotic nature of human interactions. Following a Con- versational Analysis perspective, we believe that detailed analysis of interactio- nal behaviors offers opportunities for socially interactive robots design impro- vements, that is: identify and reproduce human ordinary skills in order to make the machines more adaptable.
Notes