Magazine article AI Magazine

Join Us for the AAAI 2020 Spring Symposium!

Magazine article AI Magazine

Join Us for the AAAI 2020 Spring Symposium!

Article excerpt

The AAAI Spring Symposium Series is an annual set of meetings run in parallel at a common site. It is designed to bring colleagues together in an intimate forum while at the same time providing a significant gathering point for the AI community. The two and one half day format of the series allows participants to devote considerably more time to feedback and discussion than typical one-day workshops. It is an ideal venue for bringing together new communities in emerging fields.

The symposia are intended to encourage presentation of speculative work and work in progress, as well as completed work. Most symposia provide ample time for discussion, and some feature novel programming, including the use of target problems, open-format panels, working groups, or breakout sessions. All symposia are limited in size, and participants are expected to attend a single symposium.

The Thirty-Second AAAI Spring Symposium Series will be held March 23-25, 2020 at Stanford University, in cooperation with the Stanford Computer Science Department. The eight symposia are as follows:

   Applied AI in Healthcare: Safety, Community,
   and the Environment
   Organized by Rajan Puri, Samira Rahimi,
   and Selma Sabanovic

   Artificial Intelligence in Manufacturing
   Organized by Mark Maybury, Peter Friedland,
   Jim Header, John Manferdelli, and
   Manish Mehta]

   AI Welcomes Systems Engineering:
   Towards the Science of Interdependence
   for Autonomous Human-Machine

   Organized by William Lawless, Ranjeev
   Mittu, Don Sofge, Thomas Shortell, Tom
   McDermott, and Brian Jalaian

   Challenges and Opportunities for
   Multi-Agent Reinforcement Learning
   Organized by Chris Amato, Frans Oliehoek,
   and Karl Tuyls

   Combining Artificial Intelligence and
   Machine Learning with Physical Sciences

   Organized by Jonghyun Lee, Eric Darve,
   Peter Kitanidis, Matthew Farthing, and
   Tyler Hesser

   Combining Machine Learning and
   Knowledge Engineering in Practice

   Organized by Andreas Martin, Knut
   Hinkelmann, Frank Van Harmelen, Doug
   Lenat, Aurona Gerber, and Hans-Georg Fill. … 
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