CALL FOR PAPERS
**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
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 environment 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 space.
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) community.
II. Keynote Speakers
- Beata Joanna Grzyb – Radboud University – The Netherlands
- Thomas Hermann– Bielefeld University – Germany
- Tetsuya Ogata – Waseda University – Japan
- Erhan Oztop – Ozyegin Universiy – Turkey
- Stefan Wermter – University of Hamburg – Germany
III. Submission
- For paper submission, use the following EasyChair web link: Paper Submission.
- Use the ACM SIGCHI format: ACM SIGCHI Templates.
- Submitted papers should be limited to 2-4 pages maximum.
The primary list of topics covers the following points (but not limited to):
- 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
- Paper submission: 15-September-2017
- Notification of acceptance: 25-September-2017
- Camera-ready version: 5-October-2017
- Workshop: 17-October-2017
V. Organizers
- Takato Horii – Osaka University – Japan
- Amir Aly – Ritsumeikan University – Japan
- Yukie Nagai – National Institute of Information and Communications Technology – Japan
- 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
Japan