A study on search techniques in the game-tree

Abstract

Artificial intelligence has developed a lot in the game field and search techniques on game-trees are essential for AI-game-playing. As many techniques for searching the game-trees have been published, the time consumption of search has decreased and the accuracy of it has increased. This paper would provide a comprehensive review of existing algorithms and improvements, including their mechanisms and performances. These techniques are first divided into two sections, techniques based on Alpha-Beta pruning and techniques based on Monte-Carlo tree search. Furthermore, techniques based on Alpha-Beta pruning are further subdivided into narrow window properties and information from previous searches based on the specific aspects they focus on. Finally, this paper concludes by summarizing the performance of these techniques and identifying their suitable application scenarios, as well as suggesting potential directions for future research.