Important Dates

July 5, 2018 (Firm Deadline)

Late-Breaking Innovation Paper Submission Due

April 8, 2018 (Closed)

Workshop Proposal Due

June 20, 2018 (Closed)

Special Session Proposal Due

May 15, 2018 (Closed)

Paper Submission Deadline

June 20, 2018 (Closed)

Industry Session Paper Submission Deadline

June 20, 2018 (Closed)

WiP Paper Submission Due

June 20, 2018 (Closed)

Poster/Demo Submission Due

June 25, 2018

Authors Notification (for all papers submitted before May 30, 2018)

July 8, 2018

Authors Notification (for all papers submitted after May 30, 2018)

August 8, 2018

Camera-ready & Registration



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Keynote Speeches


You are Welcome to Attend Keynote Speeches Specific to IEEE IoP 2018 as in Part I.


You are also Welcome to Attend all Keynote Speeches Shared by Co-Located IEEE Smart World Congress 2018 & IEEE UIC 2018 & IEEE ATC 2018 & IEEE ScalCom 2018 & IEEE CBDCom 2018 & IEEE SCI 2018 Conferences as in Part II.


Part I.

Keynote Speaker 1: Prof. Fuhua (Oscar) Lin, Athabasca University, Canada
Title: Multi-Agent Systems with Reinforcement Learning


Detailed Information about Keynote Speeches


Title: Multi-Agent Systems with Reinforcement Learning

Keynote Speaker 1: Prof. Fuhua (Oscar) Lin

Athabasca University, Canada







Abstract
. Distributed competitive decision making, as opposed to centralized planning, is emerging as the norm in networked systems which involve repeatedly making decisions in an uncertain environment. In this keynote presentation I will present Multi-Agent Systems with Reinforcement Learning for decision-making for such systems, taking into account rationally selfish behavior of the connected agents/minds. In such environments, agents need to consider how to compete for scarce resources, trade, negotiate automatically, learn from each other, and form social organizations. In particular, I will discuss multi-armed bandit (MAB) learning algorithms. Unlike standard black-box and big-data based machine learning tools, MAB algorithms are active learning, which refers to algorithms that actively select data they should receive, and online learning, which refers to algorithms that analyze data in real-time and provide results on the fly. I will present MAB two real-world applications: Online Scheduling in Oil and Gas Industry and Vehicle Routing for Driverless Cars in Smart City.

Biography. Fuhua Lin is a professor in computing and information systems, Faculty of Science and Technology at Athabasca University, Canada. His main research interests are: intelligent systems, multiagent systems, game theory, virtual reality, machine learning, online learning technologies and online scheduling. He has published more than 100 papers in international journals, proceedings of international conferences, books, and book chapters. Dr. Lin obtained his PhD in Virtual Reality from the Hong Kong University of Science and Technology in 1998. Prior to working in Athabasca University, Dr. Lin was a Research Officer of Institute for Information Technology of National Research Council (NRC) of Canada. Dr. Lin did post-doc research at University of Calgary during 1998-1999. Dr. Lin has acted as Principal and co-Principal Investigator on two NSERC Discovery Grants, two NSERC Engage grants, one Canada Foundation for Innovation (CFI) fund of Canada. He severed as the Co-Editor-In-Chief and Editor-In-Chief of International Journal of Distance Education Technologies (IJDET) during 2009-2013. Dr. Lin got Leaders Opportunity Fund Award from CFI in 2009, Craig Cunningham Mentoring and Teaching Excellence (CCMATE) Award, from Athabasca University in 2012, and a best paper award at IEEE Computer Science and Engineering (CSE) 2014. Dr. Lin is a member of AAAI, ACM, IEEE, AIED, and CAIAC.



Part II.

More keynote speeches will be available soon.



Organizers:

  


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