Determinization and information set Monte Carlo Tree Search for the card game Dou Di Zhu

2011 ◽  
Author(s):  
Daniel Whitehouse ◽  
Edward J. Powley ◽  
Peter I. Cowling
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Card Game ◽  
Monte Carlo Tree Search ◽  
Information Set

scholarly journals Information Set Monte Carlo Tree Search

2012 ◽  
Vol 4 (2) ◽  
pp. 120-143 ◽  
Author(s):  
P. I. Cowling ◽  
E. J. Powley ◽  
D. Whitehouse
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Information Set

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 ALTERNATIVE SELECTION FUNCTIONS FOR INFORMATION SET MONTE CARLO TREE SEARCH

2014 ◽  
Vol 54 (5) ◽  
pp. 333-340
Author(s):  
Viliam Lisy
Keyword(s):  
Monte Carlo ◽  
Imperfect Information ◽  
Search Algorithm ◽  
Superior Performance ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Information Set ◽  
Imperfect Information Games ◽  
Zero Sum ◽  
Tree Search Algorithm

We evaluate the performance of various selection methods for the Monte Carlo Tree Search algorithm in two-player zero-sum extensive-form games with imperfect information. We compare the standard Upper Confident Bounds applied to Trees (UCT) along with the less common Exponential Weights for Exploration and Exploitation (Exp3) and novel Regret matching (RM) selection in two distinct imperfect information games: Imperfect Information Goofspiel and Phantom Tic-Tac-Toe. We show that UCT after initial fast convergence towards a Nash equilibrium computes increasingly worse strategies after some point in time. This is not the case with Exp3 and RM, which also show superior performance in head-to-head matches.

Parallelization of Information Set Monte Carlo Tree Search

2014 ◽  
Author(s):  
Nick Sephton ◽  
Peter I. Cowling ◽  
Edward Powley ◽  
Daniel Whitehouse ◽  
Nicholas H. Slaven
Keyword(s):  
Monte Carlo ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Information Set
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