List of Tutorials
- Title: AI for Social Good: A Multiagent Systems Perspective
Presenters: Milind Tambe, Fei Fang, Bryan Wilder, Eugene Vorobeychik
Website: http://teamcore.usc.edu/people/bryanwilder/aamas_social_good.htm
- Title: Multi-Agent Pathfinding
Presenters: Roman Barták, Roni Stern
Website: http://ktiml.mff.cuni.cz/~bartak/AAMAS2018/
- Title: Multi-objective Planning and Reinforcement Learning
Presenters: Shimon Whiteson, Diederik M. Roijers
Website: http://roijers.info/motutorial.html
- Title: Distributed Constraint Optimization for the Internet-of-Things (DCOP for IoT)
Presenters: Gauthier Picard, Pierre Rust
Website: http://www.emse.fr/~picard/dcop4iot/
- Title: Universal Reinforcement Learning
Presenters: Tom Everitt, Marcus Hutter
Website: http://www.tomeveritt.se/tutorial/2018/03/08/aamas-18-tutorial-on-universal-rl.html
- Title: Multi-agent Systems and Social Influence
Presenters: Truls Pedersen, Marija Slavkovik
Website: http://slavkovik.com/aamastutorial.html
- Title: From Game Theory to AI in Games
Presenters: Georgios Chalkiadakis, Georgios N. Yannakakis
Website:http://www.gameai.institutedigitalgames.com/aamas/
- Title: Multi-agent (Deep) Learning
Presenters: D. Balduzzi, T. Graepel. J. Perolat, K. Tuyls
Website: https://sites.google.com/site/maltutorial2018/
- Title: Combinatorial Optimization for Graphical Models
Presenters: Rina Dechter, Alexander Ihler, Radu Marinescu
Website: https://sites.google.com/view/tutorial-aamas-2018/home
Schedule
| Room Number (Time) | Tutorial |
|---|---|
| K11 (14:00-18:00) | AI for Social Good: A Multiagent Systems Perspective |
| K23 (14:00-18:00) | Combinatorial Optimization for Graphical Models |
| K24 (14:00-18:00) | Distributed Constraint Optimization for theInternet-of-Things (DCOP for IoT) |
| K22 (8:30-12:30) | From Game Theory to AI in Games |
| K23 (8:30-12:30) | Multi-Agent Pathfinding |
| K24 (8:30-12:30) | Multi-agent systems and social influence |
| K11 (8:30-12:30) | Multi-objective planning and reinforcement learning |
| K22 (14:00-18:00) | Universal Reinforcement Learning |
| K21 (8:30-12:30; 14:00-18:00) | Multi-agent Learning |
All tutorials include (as applicable according to time) coffee breaks from 10:00-10:30 and 15:30-16:00 and a lunch break from 12:30-14:00.