It is the state of the art in perfect and imperfect information games.
Other applications include the RNA inverse folding problem, Logistics, Multiple Sequence Alignment, General Game Playing, Puzzles, 3D Packing with Object Orientation, Cooperative Pathfinding, Software testing and heuristic Model-Checking.
In recent years, many researchers have explored different variants of the algorithms, their relations to Deep Reinforcement Learning and their different applications.
The purpose of this workshop is to bring these researchers together to present their research, discuss future research directions, and cross-fertilize the different communities.
Researchers and practitioners whose research might benefit from Monte Carlo Search in their research are welcome.
Monte Carlo Tree Search, and then Zero learning vastly improved Monte Carlo search in a wide range of applications; classic Monte Carlo search still dominates many partially observable problems.
Submissions are welcome in all fields related to Monte Carlo Search, including:
Acceptance Notification: May 26, 2020
Final Papers: June 26, 2020
MCS 2020: July 12, 2020
Olivier Teytaud, Facebook FAIR
Mark Winands, Maastricht University