Staff

Speaker: Zhenmei Gu

Information Extraction (IE) summarizes a collection of textual documents into a structual representation by identifying specific facts from text. The naive Bayes model is one of the first statistical models that have been applied to IE for learning extraction patterns from labelled data. In spite of the simplicity and the popularity of the naive Bayes model, we have observed a formulation problem in previous work on naive Bayes IE. In this talk, we present a formal naive Bayes modelling for IE, by which the induced formula for the filler probability estimation is more theoretically sound.

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: 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: Milen Pavlov

Research collaborations are always encouraged, as they often yield good results. However, researcher networks contain massive amounts of experts in various disciplines and it is difficult for the individual researcher to decide which experts will match his own expertise best.

Speaker: Dana Wilkinson

There are a variety of domains where it is desirable to learn a representation of an environment defined by a stream of sensori-motor experience. This talk introduces and formalizes Subjective Mapping, a novel approach to this problem.

Speaker: Yves Lesperance (York University)

In this talk, I will present a formal model of plan recognition for inclusion in a cognitive agent programming framework.

Speaker: Chun Wang (University of Western Ontario)

Many scheduling problems are inherently decentralized, such as those in supply chain management, production management, network-based information-processing environments, transportation and other types of service industries. In recent years, the importance of automated decentralized scheduling systems is increasing as a result of the significant growth of eMarkets.

Speaker: Andrea Bunt

Mixed-initiative approaches to GUI personalization combine aspects of purely adaptive approaches, which rely on AI techniques to automatically personalize the GUI, and adaptable approaches, which place the personalization onus solely on the user.

Speaker: Gerhard Lakemeyer (Department of Computer Science, RWTH Aachen, Germany)

Our group has been involved with Robocup for more than five years. We started out in the so-called mid-size league with teams of four to five mobile robots playing soccer. Recently we have also entered the Robocup@Home league, where robots operate in a home environment.

Speaker: Scott Sanner (NICTA, Australia)

Most traditional approaches to probabilistic planning in relationally specified MDPs rely on grounding the problem w.r.t. specific domain instantiations, thereby incurring a combinatorial blowup in the representation. An alternative approach is to lift a relational MDP to a first-order MDP (FOMDP) specification and develop solution approaches that avoid grounding.