Categories |
MONTE CARLO SEARCH
ARTIFICIAL INTELLIGENCE
GAMES
|
About |
|
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. |
Call for Papers |
|
Submissions are welcome in all fields related to Monte Carlo Search, including:
|
Summary |
|
MCS 2020 : Monte Carlo Search, IJCAI Workshop will take place in Yokohama, Japan. It’s a 7 days event starting on Jul 11, 2020 (Saturday) and will be winded up on Jul 17, 2020 (Friday). MCS 2020 falls under the following areas: MONTE CARLO SEARCH, ARTIFICIAL INTELLIGENCE, GAMES, etc. Submissions for this Workshop can be made by Apr 26, 2020. Authors can expect the result of submission by May 26, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Jun 26, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the MCS 2020
|
Credits and Sources |
| [1] MCS 2020 : Monte Carlo Search, IJCAI Workshop |
