Staff

Speaker: Robin Cohen

In this paper, we discuss the importance of modeling the potential bother to the user, when reasoning about interaction in a mixed-initiative setting. We summarize our previous work on modeling bother as one of the costs of interaction, clarifying how to incorporate this estimated cost when reasoning about whether to initiate interaction.

Speaker: Rob Warren

Ontology research has had a renewed interest with the release of two ontological markup standards, DAML+OIL and OWL, in December of 2001 and March of 2002.

In this talk I'll review some of the work I did while at the Fungal Web Project in Montreal, where we faced several questions about the use and adoption of ontologies and their markup languages:

Speaker: Kevin Regan

This paper examines approaches to representing uncertainty in reputation systems for electronic markets with the aim of constructing a decision theoretic framework for collecting information about selling agents and making purchase decisions in the context of a social reputation system.

Speaker: Shai Ben-David

Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first understanding of their data by identifying meaningful groups among the data points.

Speaker: Tyrel Russell

Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem in instruction scheduling is to find a minimum length schedule for a basic block---a straight-line sequence of code with a single entry point and a single exit point---subject to precedence, latency, and resource constraints. Solving the problem exactly is known to be difficult, and most compilers use a greedy list scheduling algorithm coupled with a heuristic.

Speaker: Adam Milstein

Localization is the problem of determining a s location in an environment. Monte Carlo Localization (MCL) is a method of solving this problem by using a partially observable Markov decision process to find the s state based on its sensor readings, given a static map of the environment. MCL requires a model of each sensor in order to work properly. One of the most important sensors involved is the estimation of the s motion, based on its encoders that report what motion the robot has performed.

Speaker: Edward Chrzanowski

This talk will present the path I traversed from metaheuristics to parallel metaheuristics in the search of a better scheduler for High Performance Computing.

Speaker: Milen Pavlov

When applying reinforcement learning to real world problems it is desirable to make use of any prior information to speed up the learning process. Unfortunately, there does not exist any framework for encoding prior knowledge in RL nor developing general algorithms that make use of it.

Speaker: Pascal Poupart

In this presentation, I will talk about the oldest form of Reinforcement Learning (RL): Bayesian Reinforcement Learning (BRL).

Speaker: Harold Boley (University of New Brunswick and Institute for Information Technology, National Research Council of Canada)

The Semantic Web contributes to making AI mainstream. AI knowledge representations such as ontologies and rules can be used to formalize Web metadata vocabularies, where constants, classes, and relations carry (Uniform Resource Identifier) URIs. This permits AI reasoning over metadata both for direct question answering and high-precision information (URI) retrieval.