Reasoning and Planning under uncertainty and resource constraints -- Theory and Applications

Speaker:        Dr. Pascal Poupart
                Visiting Scholar,HKUST
                Associate Professor
                David R. Cheriton School of Computer Science
                University of Waterloo

Title:          "Reasoning and Planning under uncertainty and resource
                 constraints -- Theory and Applications"

Date:           Monday, 9 March 2015

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lifts 25/26), HKUST

Abstract:

Recent advances in planning techniques have focused on online search
techniques. While these techniques allow practitioners to obtain policies
for fairly large problems, they assume that a non-negligible amount of
computation can be done between each decision point. In contrast, the
recent proliferation of mobile and embedded devices has lead to a surge of
applications that could benefit from state of the art planning techniques
if they can operate under severe constraints on computational resources.
For instance, consider the emerging class of monitoring and assistive
applications that run on smart-phones, wearable systems or other mobile
devices.  While computational resources are rapidly increasing, energy
consumption remains an important bottleneck due to limited battery life.
In this talk, I will present some experiments about battery consumption
for various types of policies using a Nexus 4 phone. I will then present
recent results for the optimization and compilation of policies into
controllers with negligible energy consumption during their execution.  I
will also present recent advances in multi-objective optimization to
tradeoff the utility and energy costs resulting from different actions.


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Biography:

Pascal Poupart is currently a Visiting Scholar at Hong Kong University of
Science and an Associate Professor in the David R. Cheriton School of
Computer Science at the University of Waterloo, Waterloo (Canada).  He
received the B.Sc. in Mathematics and Computer Science at McGill
University, Montreal (Canada) in 1998, the M.Sc. in Computer Science at
the University of British Columbia, Vancouver (Canada) in 2000 and the
Ph.D. in Computer Science at the University of Toronto, Toronto (Canada)
in 2005.  His research focuses on the development of algorithms for
reasoning under uncertainty and machine learning with application to
Assistive Technologies and Natural Language Processing.  He is most
well-known for his contributions to the development of approximate
scalable algorithms for partially observable Markov decision processes
(POMDPs) and their applications in real-world problems, including
automated prompting for people with dementia for the task of handwashing
and spoken dialog management. Other notable projects that his research
team are currently working on include chatbots for automated personalized
conversations and a wearable sensor system to monitor and prompt users to
participate in non-sedentary activities.

Pascal Poupart received the Early Researcher Award, a competitive honor
for top Ontario researchers, awarded by the Ontario Ministry of Research
and Innovation in 2008.  He was also a co-recipient of the Best Paper
Award Runner Up at the 2008 Conference on Uncertainty in Artificial
Intelligence (UAI) and the IAPR Best Paper Award at the 2007 International
Conference on Computer Vision Systems (ICVS).  He served on the editorial
board of the Journal of Artificial Intelligence Research (JAIR) (2008
2011) and the Journal of Machine Learning Research (JMLR) (2009 present).
His research collaborators include Google, Intel, Huawei, Kik Interactive,
In the Chat, Slyce.it, the Alzheimer Association, the UW-Schlegel Research
Institute for Aging, Sunnybrook Health Science Centre, the Toronto
Rehabilitation Institute and the Intelligent Assistive Technology and
Systems Laboratory at the University of Toronto.