[iva] Fwd: CFP: AAMAS 2019 Workshop on Cognitive Architectures for HRI: Embodied Models of Situated Natural Language Interactions (MM-Cog)

[Brigitte Krenn]

* Apologies for cross postings *


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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
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Overview
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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
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  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 
manipulation)?

* 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 
abstraction?

* 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
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* 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
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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.


Important Dates
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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.


Organizers
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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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