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Mon, 17 Dec 2018 12:44:45 +0100
"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Stephanie Gross <[log in to unmask]>
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* Apologies for cross postings *


Workshop on Cognitive Architectures for Human-Robot Interaction:
Embodied Models of Situated Natural Language Interactions (MM-Cog)
May 13th or 14th 2019, in Montreal, Canada
Paper submission deadline: February 12, 2019


The workshop will take place in conjunction with the International
Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019) in
Montreal, Canada.
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 and social interaction, we are interested in mechanisms
for representation and acquisition, memory structures etc., up to full
models of socially guided, situated, multi-modal language interaction.
These models can then be used to test theories of human situated
multi-modal interaction, as well as to inform computational models in
this area of research.

Call for Papers

 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:

* Which kind of data is adequate to develop socially guided models of
language acquisition, e.g. multi-modal interaction data, audio, video,
motion tracking, eye tracking, force data (individual or joint object

* How should empirical data be collected and preprocessed in order to
develop socially guided models of language acquisition, e.g. collect
either human-human or human-robot data?

* Which mechanisms are needed by the artificial system to deal with the
multi-modal complexity of human interaction. And how to combine
information transmitted via different modalities - at a higher level of

* Models of language learning through multi-modal interaction: How
should semantic representations or mechanisms for language acquisition
look like to allow an extension through multi-modal interaction?

* Based on the above representations, which machine learning approaches
are best suited to handle the multi-modal, time-varying and possibly
high dimensional data? How can the system learn incrementally in an
open-ended fashion?

Relevant Topics include (but are not limited to) the following

* models of embodied language acquisition

* models of situated natural language interaction

* multi-modal situated interaction data

* individual / joint manipulation & task description data

* multi-modal human-human interaction

* multi-modal human-robot interaction

* acquiring multi-modal semantic representations

* multi-modal reference resolution

* machine learning approaches for multimodal situated interaction

* embodied models of incremental learning

Invited Speakers

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

Important Dates

Paper submission deadline: February 12, 2019

Notification of acceptance: March 10, 2019

Final version: March 20, 2019

Workshop: May 13 or 14, 2019

Articles should be 4-6 pages, formatted using the AAMAS 2019 Author's
Kit. For each accepted contribution, at least one of the authors is
required to attend the workshop. Authors are invited to submit their
manuscripts in PDF.


Stephanie Gross, Austrian Research Institute for Artificial
Intelligence, Vienna, Austria
Brigitte Krenn, Austrian Research Institute for Artificial Intelligence,
Vienna, Austria
Matthias Scheutz, Department of Computer Science at Tufts University,
Massachusetts, USA
Matthias Hirschmanner, Automation and Control Institute at Vienna
University of Technology, Vienna, Austria

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