The 2017 International Workshop on Knowledge Representation: Brain and Machine (KRBM 2017)

November 16th, 2017 in Beijing, China

 

 

 

** Introduction **

Knowledge processing and representation has been extensively studied in at least two fields – cognitive neuroscience (and psychology) with human brain as the research target and artificial intelligence with machine implementation as the target. These two fields have converged and have diverged in the past. The exciting flourish that the recent years have witnessed in both disciplines has convinced us that it is time for convergent discussions again.

More specifically, understanding how the brain represents and process knowledge is a grand challenge for Cognitive Science, Cognitive Psychology, and Neuroscience. In addition, there is still a big gap for knowledge and language processing in brains and machines, since the fundamental mechanisms that support brains and CURRENT machines to represent and process knowledge seem to be fundamentally different. The common interest on knowledge representation and processing for Brain and Artificial Intelligence research are calling for joint efforts. The possible output of this effort is not only deeper and more comprehensive understanding on how the brain represent and process knowledge, but also with a future brain-inspired model for knowledge representation and processing. This workshop aims at bringing researchers from Brain Science, Cognitive Science, Cognitive Psychology, Artificial Intelligence, etc., to discuss how they can collaborate and inspire each other to advance the understanding and application of knowledge representation and reasoning from the Brain and the Machine perspectives. The workshop will be co-located with the 2017 International Conference on Brain Informatics, November 16th, 2017 in Beijing, China.

 

** Topics of Interests **

Research contributions should be related but are not limited to one or more of the following topics:

  • Cognitive and Neural Basis for Declarative Knowledge
  • Cognitive and Neural Basis for Procedure Knowledge
  • Learning and Memory mechanism for Human Knowledge
  • Multi-sensory Knowledge Learning
  • Computational Model for Language Understanding
  • Computational Model for knowledge representation
  • Brain-inspired Neural Network for Language Processing
  • Brain-inspired Neural Network for Knowledge Representation
  • Brain-inspired Neural Network for Reasoning

** Invited Speakers**

 
 
 

Domenica Romagno

University of Pisa, Italy

Qiufang Fu

Institute of Psychology,
Chinese Academy of Sciences

Xiaosha Wang

Beijing Normal University

Yi Zeng

Institute of Automation,
Chinese Academy of Sciences

       
Conceptual Representations and Linguistic Categories in the Brain: The case of Word Classes
Which matters more in implicit category learning: edge-based vs. surface-based features
Organizational principles of abstract words in the human brain
Brain-inspired Declarative Knowledge Acquisition, Representation and Reasoning

 

** Workshop Co-Chairs **

Yanchao Bi,

IDG/McGovern Institute for Brain Research &
State Key Laboratory of Cognitive Neuroscience and Learning 
Beijing Normal University, China

Yi Zeng,

Institute of Automation &
Center for Excellence in Brain Science and Intelligence Technology,
Chinese Academy of Sciences, China.