In many situations, a sequence of decisions must be taken by an individual
or system (e.g., course selection by students, inspection of parts for
testing in a factory, etc.). However, deciding on a course of action is
notoriously difficult when there is uncertainty in the effects of the
actions and the objectives are complex. Markov decision processes (MDPs)
provide a principled approach for automated planning under uncertainty.
While the beauty of an automated approach is that the computational power of
machines can be harnessed to optimize difficult sequential decision making
tasks, the drawback is that users no longer understand why certain actions
are recommended. This lack of understanding is a serious bottleneck that is
currently holding back the widespread use of automated tools such as MDPs in
recommender systems. Hence, there is a need for explanations that enhance
the user's understanding and trust of these recommendations.
In this
talk, I will present a generic technique to explain policies in arbitrary
domains where the sequential decision making problem is formulated as a
factored Markov decision process. The explanations consist of template
sentences that are filled with relevant information to justify why some
action was recommended in a given state. I will describe a mechanism to
determine a minimal set of templates that are sufficient to completely
justify the action choice. The approach will be demonstrated and evaluated
with a user study in the context of advising undergraduate students in their
course selection.
Joint work with Omar Zia Khan and James Black
Reference:
Minimal Sufficient Explanations for Factored Markov Decision Processes. Omar
Zia Khan, Pascal Poupart and James Black. International Conference on
Automated Planning and Scheduling (ICAPS), Thessaloniki, Greece, 2009.
Pascal Poupart is 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, Natural
Language Processing and Information Retrieval. 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 a smart walker to assist older people and a wearable
sensor system to assess and monitor the symptoms of Alzheimer's disease.
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 is a member of the editorial board of the
Journal of Artificial Intelligence Research (JAIR) and the Journal of
Machine Learning Research (JMLR). His research collaborators include Google,
Intel, AideRSS, 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.