Events

Speaker: Hojjat Ghaderi, University of Toronto

The coordination of cooperating but autonomous agents is a core problem in multiagent systems research. A team of agents is said to have joint ability to achieve a goal if despite any incomplete knowledge or even false beliefs that they may have about the world or each others, they still know enough to get to a goal state, should they choose to do so.

Speaker: Thomas Zeugmann, Hokkaido University, Japan

Clustering algorithms working with a matrix of pairwise similarities (kernel matrix) for the data are widely known and used, a particularly popular class being spectral clustering algorithms. In contrast, algorithms working with the pairwise distance matrix have been studied rarely for clustering. This is surprising, as in many applications, distances are directly given, and computing similarities involves another step that is error-prone, since the kernel has to be chosen appropriately, albeit computationally cheap.

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.