scholarly journals Challenging Human Supremacy: Evaluating Monte Carlo Tree Search and Deep Learning for the Trick Taking Card Game Jass

2020 ◽  
pp. 505-517
Author(s):  
Joel Niklaus ◽  
Michele Alberti ◽  
Rolf Ingold ◽  
Markus Stolze ◽  
Thomas Koller
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Tree Search ◽  
Card Game ◽  
Monte Carlo Tree Search

Design of a Block Go program using deep learning and Monte Carlo tree search

ICGA Journal ◽  
2018 ◽  
pp. 1-11
Author(s):  
Ching-Nung Lin ◽  
Jr-Chang Chen ◽  
Shi-Jim Yen ◽  
Chan-San Chen
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Tree Search ◽  
Monte Carlo Tree Search

scholarly journals IMPLEMENTASI MONTE CARLO TREE SEARCH PADA PERMAINAN KARTU “DAIFUGO”

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Eunike Thirza Hanitya Christian ◽  
R. Gunawan Santoso ◽  
Erick Purwanto
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Card Game ◽  
Optimal Result ◽  
Monte Carlo Tree Search ◽  
Random Simulation ◽  
Simulation Monte Carlo ◽  
Step Selection

Daifugo is climbing card game that is originated from Japan. AI player of Daifugo card game can be implemented using Monte Carlo Tree Search to get optimal result from random simulation. Monte Carlo Tree Search has 4 step, selection, expansion, simulation and backpropagation that is executed until maximal loop is reached. Objective of using Monte Carlo Tree Search on AI player in Daifugo card game is to get move with high winning rate and to observe the effect of number of loop on the method to winning rate

scholarly journals De Novo Prediction of RNA 3D Structures with Deep Learning

2021 ◽  
Author(s):  
Julius Ramakers ◽  
Christopher Frederik Blum ◽  
Sabrina König ◽  
Stefan Harmeling ◽  
Markus Kollmann
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Nucleic Acid ◽  
Predictive Power ◽  
De Novo ◽  
Learning Approach ◽  
Tree Search ◽  
Blind Test ◽  
Monte Carlo Tree Search ◽  
Score Model

We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a Score Model to find and rank the most likely folding structures for a given RNA sequence. We confirm the predictive power of our approach by setting new benchmarks for some longer sequences in a simulated blind test of the RNA Puzzles prediction challenge.

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