Solving Nurikabe with Monte-Carlo Tree Serach

被引：3
|
作者：
Futuhi, Ehsan [1 ]
Karimi, Shayan [2 ]
机构：
[1] Amirkabir Univ, Dept Comp Engn, Tehran, Iran
[2] Amirkabir Univ, Dept Elect Engn, Tehran, Iran
来源：
2020 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA 2020) | 2020年
关键词：
AI; reinforcment learning; puzzle; time; memory; monte-carlo;
D O I：
10.1109/ICCIA49625.2020.00014
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Puzzle solving with AI is becoming one of the hot topic fields in computer science. The algorithmic challenges that are laid behind this topic, make it more attractive. One of these NP-complete puzzles that is hard to solve for human being is Nurikabe Puzzle. few methods have been developed for solving this puzzle that have a poor performance in time and memory. Monte-Carlo Tree Search(MCTS) is a famous reinforcement algorithm that have been used in many Logical games.In this article we use Monte-Carlo Tree Search method for creating the efficient method that performs well on time that it takes for solving the puzzle.no one have ever used this method for solving this problem and also we test our algorithm with a wide range of test cases from easy to hardest ones.
引用
收藏
页码：33 / 38
页数：6
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