Faculty

Speaker: Yiyu Yao, University of Regina

As an emerging field of study, granular computing has received much attention. Many models, frameworks, methods and techniques have been proposed and studied. It is perhaps the time to seek for a general and unified view so that fundamental issues can be examined and clarified. This talk examines granular computing from three perspectives.

Speaker: Laurent Charlin

Planning in partially observable domains is a notoriously difficult problem. However, in many real-world scenarios, planning can be simplified by decomposing the task into a hierarchy of smaller planning problems. Several approaches have been proposed to optimize a policy that decomposes according to a hierarchy specified a priori. In this thesis, I investigate the problem of automatically discovering the hierarchy.

Speaker: Tyrel Russell

Sports fans in many sports anxiously watch their team's performances and their chances of winning a championship or securing a playoff spot. Typically, they obtain their information from major newspapers and websites, which publish standings along with remarks on the qualification and elimination of the individual teams.

Speaker: Daniel Saunders (Queens University)

There has been much debate about the nature of the processes involved in biological motion perception. In contrast to previous views, Jastorff et al. (2007) provided evidence that biological motion is understood via a mechanism that is specialized, but also relatively plastic, alterable over the course of hours rather than years.

Speaker: Omar Zia Khan

There has been little work in explaining recommendations generated by Markov Decision Processes (MDPs). We analyze the difficulty of explaining policies computed automatically and identify a set of templates that can be used to generate explanations automatically at run-time.

Speaker: Babak Alipanahi

There has been much recent attention to the problem of learning an appropriate distance metric, using class labels or other side information. Some proposed algorithms are iterative and computationally expensive.

Speaker: Sandip Sen, University of Tulsa

We believe that intelligent information agents will represent their users interest in electronic marketplaces and other forums to trade, exchange, share, identify, and locate goods and services.

Speaker: Enrico Gerding, University of Southampton

Scoring rules are originally used to reward probabilistic estimates such as weather forecasts, where the score or utility that an agent receives depends on the materialized outcome of the prediction. Strictly proper scoring rule are a class of scoring rule which are designed such that they incentivize utility-maximizing agents to reveal the entire probability distribution truthfully.

Speaker: Pascal Poupart, Allan Caine, Farheen Omar and Adam Hartfield

Walkers are becoming an increasingly popular mobility aid among older adults. While they are designed to improve balance, the fact that many walkers have wheels, it is not clear whether stability is enhanced or jeopardized.

Speaker: Kevin Regan, University of Toronto

Traditional methods for finding optimal policies in stochastic, multi-step decision environments require a precise model of both the environment dynamics and the rewards associated with taking actions and the effects of those actions.