Events

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.

Speaker: Kate Larson

Since its introduction in the mid-nineties, Dung's theory of abstract argumentation has been influential in AI as it serves as a convenient model for reasoning about general characteristics of argument.

Speaker: Reid Kerr

Traders in electronic marketplaces may behave dishonestly, cheating other agents. A multitude of trust and reputation systems have been proposed to try to cope with the problem of cheating.

Speaker: Igor Kiselev

Classical dynamic approaches to online learning and optimization address the issue of statistical fluctuations of the incoming data by means of continual retraining their models, which is computationally intractable for real-world problems of practical interest and is inappropriate in time-critical scenarios.

Speaker: Andrew Seniuk and Dorothea Blostein (Queens University)

Handwriting and speech recognition are problems with a long history. However, no studies have considered the sounds produced by handwriting, an information source which has connections to both of the aforementioned.

Speaker: Tyrel Russell

This talk will discuss the application of constraint programming to the problems of deciding when a team has qualified and, if not, what conditions must be satisfied in order for them to qualify.

Speaker: Rita Ackerman

Aiming towards the development of a general clustering theory, we discuss abstract axiomatization for clustering. In this respect, we follow up on the work of Kleinberg, (Kleinberg, 2002) that showed an impossibility result for such axiomatization.

Speaker: Greg Hines

In much of the literature in multiagent systems, researchers assume (either implicitly or explicitly) that the agents are risk-neutral. However, this assumption is not always warranted in that agents can be risk-averse.

Speaker: Bowen Hui, University of Toronto

Due to its increasing complexity, there is a need to adapt software in order to maximize end-user experience. In this talk, I will describe my PhD work on developing and learning a user model in the domain of intelligent assistance.

Speaker: Lachlan Dufton, University of Waterloo

Policy teaching through environment design is a new framework that allows an interested party to modify an agent's environment by providing incentives, in order to achieve a desired behaviour.