Fuzzy Logic for Artificial Intelligence
~Contributions and Advantages~

Atsushi Inoue, Ph.D.,

Professor of Information Systems and Business Analytics, Eastern Washington University, USA

CTO & Global R&D Leader,
BaaSid Project, the Pas Pacific Venture in JP, KR, HK, TW, SG, AU & US


This talk addresses what and how Fuzzy Logic has contributed to the advancement of Artificial Intelligence, and why it is advantageous to be considered and indeed utilized. Such advantages are introduced within the fundamental framework of Artificial Intelligence consisting of logic and probability, and by showing how Fuzzy Sets and Logic may extend those and more (e.g. Neural Networks and Evolutionary Computing). Applications relevant to Image Processing and Robotics are introduced within this extended framework. Last but not the least, such a framework does indeed demonstrate the consistency between computing in numbers and human reasoning in languages -- so-called Computing with Words by Professor Lotfi A Zadeh.

atsushi1 atsushi2


Atsushi Inoue is an Artificial Intelligence specialist (especially in Fuzzy Logic) in USA and Japan. He has been affiliated with top-notch industries and institutes in several countries, including Hitachi Ltd. (Japan) and Carnegie Mellon University (USA). He is currently home at Eastern Washington University to enjoy his life in the beautiful evergreen, while advising various digital entrepreneurships and intelligent system projects. Recently, he is participating in the global blockchain startup for advancing our lives in the severe information centered society. He earned his Ph.D. in Computer Science and Engineering at the University of Cincinnati (USA) in 1999.

Machine Learning for Socially Assistive Intelligent Robots Operating in Human Environments

Prof. Genci Capi,

Department of Mechanical Engineering,
Hosei University, Japan


The research on intelligent robots will produce robots that are able to operate in everyday life environments, to adapt their policy as environment changes, and to cooperate with other team members and humans. Operating in human environments the robots have to be process in real time a large number of sensory data such as vision, laser, microphone, in order to determine the best action. Learning and evolution have been proved to give good results generating a good mapping of various sensory data to robot action.

In this talk, I will overview the existing efforts including our attempts at creating intelligent robots operating in everyday life environments. In particular, I will focus on remotely operating surveillance robot, robot navigation in urban environments, and assistive humanoid robot. I will show experimental results that demonstrate the effectiveness of proposed algorithms.



Genci Capi received the Ph.D. degree from Yamagata University, in 2002. He was a Researcher at the Department of Computational Neurobiology, ATR Institute from 2002 to 2004. In 2004, he joined the Department of System Management, Fukuoka Institute of Technology, as an Assistant Professor, and in 2006, he was promoted to Associate Professor. He was a Professor in the Department of Electrical and Electronic Systems Engineering, University of Toyama up to March 2016. Now he is a Professor in the Department of Mechanical Engineering, Hosei University. His research interests include intelligent robots, BMI, multi robot systems, humanoid robots, learning and evolution.