The proceedings will be published with Springer in their Communications in Computer and Information Science series CCIS.
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:
Erwan Le Merrer and Adel Jaouen, zoNNscan: A boundary-entropy index for zone inspection of neural models
Tobias Joppen and Johannes Furnkranz, Ordinal Monte Carlo Tree Search
Tristan Cazenave and Veronique Ventos, The AlphaMu Search Algorithm for the Game of Bridge
Tristan Cazenave, Monte Carlo Game Solver
Tristan Cazenave, Generalized Nested Rollout Policy Adaptation
Florian Geisser, David Speck and Thomas Keller, Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces
Sunandita Patra, James Mason, Amit Kumar, Malik Ghallab, Paolo Traverso and Dana Nau, Integrating Acting, Planning, and Learning in Hierarchical Operational Models
Tristan Cazenave and Thomas Fournier, Monte Carlo Inverse Folding
Tristan Cazenave, Jean-Baptiste Sevestre and Matthieu Toulemont, Stabilized Nested Rollout Policy Adaptation
Tristan Cazenave, Benjamin Negrevergne and Florian Sikora, Monte Carlo Graph Coloring
Chiara F. Sironi, Tristan Cazenave and Mark H. M. Winands, Enhancing Playout Policy Adaptation for General Game Playing
Final Papers: TBA
MCS 2020: January, 2021
Olivier Teytaud, Facebook FAIR
Mark Winands, Maastricht University
Yngvi Bjornsson Reykjavik University
Bruno Bouzy Nukkai
Cameron Browne Maastricht University
Tristan Cazenave University Paris-Dauphine
Stefan Edelkamp King's College London
Raluca Gaina Queen Mary University of London
Aurelien Garivier ENS Lyon
Reijer Grimbergen Tokyo University of Technology
Nicolas Jouandeau University Paris 8
Emilie Kaufmann CNRS
Jakub Kowalski University of Wroclaw
Marc Lanctot Google DeepMind
Jialin Liu Southern University of Science and Technology
Martin Mueller University of Alberta
Andrzej Nagorko University of Warsaw
Benjamin Negrevergne University Paris-Dauphine
Santiago Ontanon Drexel University
Diego Perez-Liebana Queen Mary University of London
Mike Preuss Leiden University
Thomas Runarsson University of Iceland
Abdallah Saffidine University of New South Wales
Spyridon Samothrakis University of Essex
Chiara Sironi Maastricht University
Fabien Teytaud Universite Littoral Cote d'Opale
Olivier Teytaud Facebook FAIR
Ruck Thawonmas Ritsumeikan University
Jean-Noel Vittaut Sorbonne University
Mark Winands Maastricht University
I-Chen Wu National Chiao Tung University