発表募集 CFP – HAI 2017 Workshop: Representation Learning for Human and Robot Cognition (2017/08/30)


HAI2017にて開催されますワークショップ「Representation Learning for Human and Robot Cognition」の発表論文の投稿締切を延長しましたので再度連絡させていただきます.

10月に開催されます人とエージェントのインタラクションに関する国際会議HAI2017 において,Workshop on Representation Learning for Human and Robot Cognitionを開催いたします.

HAI2017 workshop on “Representation Learning for Human and Robot Cognition”


なおこのワークショップは,JST CREST「認知ミラーリング:認知過程の自己理解と社会的共有による発達障害者支援」とJST CREST「記号創発ロボティクスによる人間機械コラボレーション基盤創成」の共催となっております.


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

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 (
2. Use the ACM SIGCHI format: ACM SIGCHI Templates (
3. 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

1. Paper submission: 15-September-2017
2. Notification of acceptance: 25-September-2017
3. Camera-ready version: 5-October-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




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