报告题目：KID Model – A Cognitive Approach to Machine Intelligence
报告人：黄润和 (Runhe Huang)，博士，终身正教授, 博导
报告人单位：日本法政大学计算机和信息科学学院
时间：2017年5月5日上午10:00
地点：外围投注365B-404报告厅
报告人简介：Dr. Huang received her B.Sc. in Electronics Technology from the National University of Defense Technology, China, in 1982, and her Ph.D. in Computer Science and Mathematics from the University of the West of England, UK, in 1993. She worked at the National University of Defense Technology during the period 1982-1988. In 1988, she received a Sino-Britain Friendship Scholarship for her Ph.D. study. She worked atthe University of Aizu, Japan from1993 to 1999 and has been working atHosei University, Japansince 2000. She headedthe Department of Computer Science from2008 to 2010.
Dr. Huang has been working in the field of Computer and Information Sciences since 1982. Her research fields include Artificial Intelligence, Ubiquitous Intelligence Computing, Machine Intelligence, Big Data, Cognitive Computing, and Knowledge Modeling. She is a Founding member of IEEE CS TC on Big Data, IEEE SMC TC on Cybermatics, IEEE CIS TC on Smart World, and Co-Chair of the IEEE CIS Task Force on Brain Informatics. She served as Guest Editor of a Special Issue on Big Data Analytics for Cyber-Physical Systems and as Editor Board Member of Brain Informatics, the International Journal of Big Data Intelligence (IJBDI); the International Journal of Cloud Computing (IJCC); Journal of Ubiquitous Computing and Intelligence (JUCI), Journal of Autonomic and Trusted Computing (JoATC); etc.She has been active in International Conferences as a Steering Committee Member, Advisory Board member,Program Co-chair; Video Contest Co-Chair; Award Chair; Special Issue Co-Chair, etc. She has published more than 180 academic refereed papers in various international conferences and journals.
报告摘要:The important functions of aMachine Intelligence (MI) are able to gain knowledge, store knowledge, and apply knowledge. As withhumans, gaining knowledgecan be achieved by interactionwith its environment. An object perceivedfrom its external world becomes meaningful information ifthe MI can retrieve associative knowledge to interpret the object. The information is assimilated into its body of knowledge if there is an identical association with the information. As a result,its body of knowledge grows in the course ofinteractionwith its external world. The KID model is proposed based on human-like information processing, i.e., a cognitive approach to Machine Intelligence. The KID model is built with three elements, i.e., D for data, I for information, and K for knowledge; three abstract functions, i.e. interpretation, assimilation, and instantiation; and a knowledge repository, i.e., K-store. This talk is to demonstrate the proposed KID model and its applications to retail business and city traffic problems.
邀请人：胡瑞敏教授
