Events

Speaker: Angele Hamel, Wilfrid Laurier University

A contract algorithm is an algorithm which is given, as part of its input, a specified amount of allowable computation time. In contrast, interruptible algorithms may be interrupted throughout their execution, at which point they must report their current solution.

Speaker: Georgia Kastidou

We introduce a framework so that communities can exchange reputation information about agents in environments where agents are migrating between communities.

Speaker: Jesse Hoey, University of Dundee

In this talk, I will present a class of devices, called ePADs, for use by art therapists working with older adults with a progressive illness such as Alzheimer's disease.

Speaker: Casba Szepesvari, University of Alberta

It is known that the nonparametric minimax rate for regression in the active and passive settings are the same for various smoothness classes. In this talk, I will look into the simpler problem when the regression domain is finite, but the response variance is location dependent, i.e., the noise is heteroscedastic.

Speaker: Meinolf Sellmann, Brown University

When dealing with real-world optimization problems, we frequently face complicated side constraints which are hard to formulate in integer programming and constraint programming.

Speaker: Tyrel Russell

This talk will have two parts. The first part will be a short talk on the work that I will be presenting at CPAIOR this year.

Speaker: Fabien Benureau

Poker research has made great progress recently, and is constantly searching for smarter way to computers play poker.

Speaker: Attendees of AAMAS 2009

Speaker: George Chalkiadakis, University of Southampton

In multiagent domains, agents form coalitions to perform tasks. The usual models of cooperative game theory assume that the desired outcome is either the grand coalition or a coalition structure that consists of disjoint coalitions (i.e., a partition of the set of agents).

Speaker: Omar Zia Khan

Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates.