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<p>* Apologies for cross postings * <br>
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<p> <br>
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<br>
<a href="http://ralli.ofai.at/workshop.html">Workshop on
Cognitive Architectures for Situated Multimodal Human Robot
Language Interaction</a> (<a
href="https://icmi.acm.org/2018/index.php?id=home">ICMI 2018</a>)<br>
October 16th, in Boulder, Colorado <br>
Extended paper submission deadline: July 13, 2018 <br>
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<p><br>
Overview<br>
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<p>The workshop will take place in conjunction with the 20th ACM
International Conference on Multimodal Interaction (ICMI 2018)
in Boulder, Colorado on the 16th of October.<br>
In many application fields of human robot interaction, robots
need to adapt to changing contexts and thus be able to learn
tasks from non-expert humans through verbal and non-verbal
interaction. Inspired by human cognition, we are interested in
various aspects of learning, including multimodal
representations, mechanisms for the acquisition of concepts
(words, objects, actions), memory structures etc., up to full
models of socially guided, situated, multimodal language
interaction. These models can then be used to test theories of
human situated multimodal interaction, as well as to inform
computational models in this area of research.</p>
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<p>Call for Papers<br>
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<p>The workshop aims at bringing together linguists, computer
scientists, cognitive scientists, and psychologists with a
particular focus on embodied models of situated natural
language interaction. Workshop submissions should answer at
least one of the following questions:<br>
<br>
* Which kind of data is adequate to develop socially guided
models of language acquisition, e.g. multimodal interaction
data, audio, video, motion tracking, eye tracking, force data
(individual or joint object manipulation)?<br>
* How should empirical data be collected and preprocessed in
order to develop cognitively inspired models of language
acquisition, e.g. should either HH or HR data be collected?<br>
* Which mechanisms are needed by the artificial system to deal
with the multimodal complexity of human interaction? How can
the information transmitted via different modalities be
combined at a higher level of abstraction?<br>
* Models of language learning through multimodal interaction:
How should semantic representations or mechanisms for language
acquisition look like to allow an extension through
multi-modal interaction?<br>
* Based on the above representations, which machine learning
approaches are best suited to handle the multimodal,
time-varying and possibly high dimensional data? How can the
system learn incrementally in an open-ended fashion?</p>
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Invited Speakers<br>
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<p>Keynotes will be given by John Laird, Professor at the
faculty of the Computer Science and Engineering Division of
the Electrical Engineering and Computer Science Department of
the University of Michigan, and Chen Yu, Professor at the
Computational Cognition and Learning Lab at Indiana
University.</p>
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<p>Important Dates<br>
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<p>Extended paper submission deadline: July 13, 2018<br>
Notification of acceptance: July 20, 2018<br>
Final version: August 3, 2018<br>
Workshop: October 16, 2018<br>
Submission Instructions<br>
Articles should be 4-6 pages, formatted using the ACM template
of the ICMI conference. For each accepted contribution, at
least one of the authors is required to attend the workshop.</p>
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<p>Organizers<br>
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Stephanie Gross, Austrian Research Institute for Artificial
Intelligence, Vienna, Austria <br>
Brigitte Krenn, Austrian Research Institute for Artificial
Intelligence, Vienna, Austria <br>
Matthias Scheutz, Department of Computer Science at Tufts
University, Massachusetts, USA <br>
Matthias Hirschmanner, Automation and Control Institute at
Vienna University of Technology, Vienna, Austria </div>
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