[agents] Craig Boutilier @ Challenges and Opportunities in Multiagent RL

[Frans Oliehoek]

Dear all,


After a fantastic inaugural presentation by Michael Bowling, we are 
excited to announce the next speaker in our virtual seminar series on 
the Challenges and Opportunities for Multiagent Reinforcement Learning 
(COMARL):


Speaker: Craig Boutilier, Google Research

Title: Maximizing User Social Welfare in Recommender Ecosystems

(abstract and bio can be found below)


Date: Thursday February 11th, 2021

Time: 17:00 CET / 16:00 UTC / 08:00 PST

Location: via google meet or youtube


For detailed instructions on how to join, please see here:

https://sites.google.com/view/comarl-seminars/how-to-attend


For additional information, please see our:

  *

    Website <https://sites.google.com/view/comarl-seminars>(includes
    schedule, instructions on how to join, etc.)

  *

    Twitter account (for speaker announcements and
    more!):@ComarlSeminars <https://twitter.com/ComarlSeminars>

  *

    Google Groups (to receive invitations):
    comarlseminars at googlegroups.com <mailto:comarlseminars at googlegroups.com>


We look forward to seeing you there!


Best regards from the organizers,


Chris Amato (Northeastern University),

Marta Garnelo (DeepMind),

Frans Oliehoek (TU Delft),

Shayegan Omidshafiei (DeepMind),

Karl Tuyls (DeepMind)




Speaker:

Craig Boutilier

Google Research,

Mountain View, CA, USA


Title:

Maximizing User Social Welfare in Recommender Ecosystems


Abstract:

An important goal for recommender systems is to make recommendations 
that maximize some form of user utility over (ideally, extended periods 
of) time. While reinforcement learning has started to find limited 
application in recommendation settings, for the most part, practical 
recommender systems remain "myopic" (i.e., focused on immediate user 
responses). Moreover, they are "local" in the sense that they rarely 
consider the impact that a recommendation made to one user may have on 
the ability to serve other users. These latter "ecosystem effects" play 
a critical role in optimizing long-term user utility. In this talk, I 
describe some recent work we have been doing to optimize user utility 
and social welfare using reinforcement learning and equilibrium modeling 
of the recommender ecosystem; draw connections between these models and 
notions such as fairness and incentive design; and outline some future 
challenges for the community.


Bio:

Craig Boutilier is a Principal Scientist at Google. He received his 
Ph.D. in Computer Science from U. Toronto (1992), and has held positions 
at U. British Columbia and U. Toronto (where he served as Chair of the 
Dept. of Computer Science). He co-founded Granata Decision Systems, 
served as a technical advisor for CombineNet, Inc., and has held 
consulting/visiting professor appointments at Stanford, Brown, CMU and 
Paris-Dauphine.

Boutilier's current research focuses on various aspects of decision 
making under uncertainty, including: recommender systems; user modeling; 
MDPs, reinforcement learning and bandits; preference modeling and 
elicitation; mechanism design, game theory and multi-agent decision 
processes; and related areas. Past research has also dealt with: 
knowledge representation, belief revision, default reasoning and modal 
logic; probabilistic reasoning and graphical models; multi-agent 
systems; and social choice.


Boutilier served as Program Chair for IJCAI-09 and UAI-2000, and as 
Editor-in-Chief of the Journal of AI Research (JAIR). He is a Fellow of 
the Royal Society of Canada (FRSC), the Association for Computing 
Machinery (ACM) and the Association for the Advancement of Artificial 
Intelligence (AAAI). He also received the 2018 ACM/SIGAI Autonomous 
Agents Research Award.

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