Designing Card Game Strategies with Genetic Programming and Monte-Carlo Tree Search: A Case Study of Hearthstone

2020 ◽  
Author(s):  
Hao-Cheng Chia ◽  
Tsung-Su Yeh ◽  
Tsung-Che Chiang
Keyword(s):  
Monte Carlo ◽  
Genetic Programming ◽  
Tree Search ◽  
Card Game ◽  
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 MONTE-CARLO EXPRESSION DISCOVERY

2013 ◽  
Vol 22 (01) ◽  
pp. 1250035 ◽  
Author(s):  
TRISTAN CAZENAVE
Keyword(s):  
Monte Carlo ◽  
Genetic Programming ◽  
Search Algorithm ◽  
Fitness Function ◽  
Benchmark Problems ◽  
Tree Search ◽  
Confidence Bounds ◽  
Local Optima ◽  
Monte Carlo Tree Search ◽  
Monte Carlo Search

Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.

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