[robotics-worldwide] [meetings] CFP - HAI 2017 Workshop: Representation Learning for Human and Robot Cognition

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[robotics-worldwide] [meetings] CFP - HAI 2017 Workshop: Representation Learning for Human and Robot Cognition

Amir Aly-2

**Apologies for cross posting **

The full day workshop:

"*Representation Learning for Human and Robot Cognition*"

In conjunction with the *5th International Conference on Human-Agent
Interaction* *- Bielefeld - Germany - October *

*17th, 2017*
*Webpage: **https://urldefense.proofpoint.com/v2/url?u=http-3A__cognitive-2Dmirroring.org_en_events_hai2017-5Fworkshop_-2A&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rStC00sfV_qVWqlcnmGhKrRoTMim5rLso-FTzVW2NfNyo&m=d2S_ks5zH5hlUbH8CDK1vKivmc4i67dtVQc-I5EWXQ8&s=EbTdJElt99tZS-7fkbik_stxjfRw151_6dlAH-Byfgs&e= 
<https://urldefense.proofpoint.com/v2/url?u=http-3A__cognitive-2Dmirroring.org_en_events_hai2017-5Fworkshop_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rStC00sfV_qVWqlcnmGhKrRoTMim5rLso-FTzVW2NfNyo&m=d2S_ks5zH5hlUbH8CDK1vKivmc4i67dtVQc-I5EWXQ8&s=VIR3LKdAEObod-ymYqBdnyDY0v3dluzoue9ZFBHFmHE&e= >

*I. Aim and Scope *Creating intelligent and interactive robots has been
subject to extensive research studies. They are rapidly moving to the
center of human environments so that they collaborate with human users in
different applications, which requires high-level cognitive functions so as
to allow them to understand and learn from human behavior. To this end, an
important challenge that attracts much attention in cognitive science and
artificial intelligence, is the “Symbol Emergence” problem, which
investigates the bottom-up development of symbols through social
interaction. This research line employs representation learning based
models for understanding language and action in a developmentally plausible
manner so as to make robots able to behave appropriately on their own. This
could open the door to robots to understand syntactic formalisms and
semantic references of human speech, and to associate language knowledge to
perceptual knowledge so as to successfully collaborate with human users in

Another interesting approach to study representation learning is “Cognitive
Mirroring”, which refers to artificial systems that could make cognitive
processes observable, such as the models that could learn concepts of
objects, actions, and/or emotions from humans through interaction. A key
idea of this approach is that robots learn individual characteristics of
human cognition rather than acquiring a general representation of
cognition. In this way, the characteristics of human cognition become
observable and can be measured as modifications in model parameters, which
is difficult to verify through neuroscience studies only.

In this workshop, we invite researchers in artificial intelligence,
cognitive science, cognitive robotics, and neuroscience to share their
knowledge and research findings on representation learning, and to engage
in cutting-edge discussions with other experienced researchers so as to
help promoting this research line in the Human-Agent Interaction (HAI)

*II. Keynote Speakers      *

   1.   *Beata Joanna Grzyb *– Radboud University – The Netherlands
   2.   *Thomas Hermann*– Bielefeld University – Germany
   3.   *Tetsuya Ogata *– Waseda University – Japan
   4.   *Erhan Oztop *– Ozyegin Universiy – Turkey
   5. *  Stefan Wermter *– University of Hamburg – Germany

*III. Submission *

   1. For paper submission, use the following EasyChair web link: *Paper
   Submission <https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Drlhrc2017&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rStC00sfV_qVWqlcnmGhKrRoTMim5rLso-FTzVW2NfNyo&m=d2S_ks5zH5hlUbH8CDK1vKivmc4i67dtVQc-I5EWXQ8&s=nlzpa5dfz7vTitI7WTafmDkHWaTSVCCh3lVT9-p6hO8&e= >*.
   2. Use the ACM SIGCHI format: *ACM SIGCHI Templates
   <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.sigchi.org_publications_chipubform&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rStC00sfV_qVWqlcnmGhKrRoTMim5rLso-FTzVW2NfNyo&m=d2S_ks5zH5hlUbH8CDK1vKivmc4i67dtVQc-I5EWXQ8&s=jyomNXZzLc1A9vERI-RQGiIsakun73cxKkGXkEjuTOQ&e= >*.
   3. Submitted papers should be limited to 2-4 pages maximum.

    The primary list of topics covers the following points (but not limited

   - Computational model for high-level cognitive capabilities
   - Predictive learning from sensorimotor information
   - Multimodal interaction and concept formulation
   - Human-robot communication and collaboration based on machine learning
   - Learning supported by external trainers by demonstration and imitation
   - Bayesian modeling
   - Learning with hierarchical and deep architectures
   - Interactive reinforcement learning

* IV. Important Dates *

   1. Paper submission: *01-September-2017*
   2. Notification of acceptance: *15-September-2017*
   3. Camera-ready version: *30-September-2017*
   4. Workshop: *17-October-2017*

*V. Organizers   *

   1.   *Takato Horii *– Osaka University – Japan
   2.   *Amir Aly *– Ritsumeikan University – Japan
   3.   *Yukie Nagai *– National Institute of Information and
   Communications Technology – Japan
   4. *  Takayuki Nagai *– The University of Electro-Communications – Japan

*Amir Aly, Ph.D.*
Senior Researcher
Emergent Systems Laboratory
College of Information Science and Engineering
Ritsumeikan University
1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577
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