Monte Carlo Search is a family of general search algorithms that have many applications in different domains.

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:

• Monte Carlo Tree Search and Upper Confidence Trees;
• Nested Monte Carlo;
• Non-locality in Monte Carlo search;
• Combination with Zero learning;
• Monte Carlo Belief-state estimation;
• Self-Adaptive Monte Carlo Search;
• Monte Carlo in many player games.
• Applications:
• Industrial applications;
• Scientific applications;
• Applications in games;
• Applications in puzzles;

Submission Guidelines

Papers are written in English using LNCS style.

Important Dates

Submission Deadline: April 26, 2020

Acceptance Notification: May 26, 2020

MCS 2020: 11-17 July 2020

Committees

Organizing committee

• Tristan Cazenave, Universite Paris-Dauphine, PSL
• Olivier Teytaud, Facebook FAIR
• Mark Winands, Maastricht University

Contact

All questions about submissions should be emailed to Tristan.Cazenave@dauphine.psl.eu