论文标题
一个新的挑战：与AI接近俄罗斯方块链接
A New Challenge: Approaching Tetris Link with AI
论文作者
论文摘要
数十年的研究已经投入了制作计算机程序，用于游戏，例如国际象棋和棋子。本文重点介绍了一款新游戏Tetris Link，这是一种仍缺乏任何科学分析的棋盘游戏。俄罗斯方块链接具有较大的分支因素，阻碍了传统的启发式计划方法。我们探索启发式计划和另外两种方法：加固学习，蒙特卡洛树搜索。我们记录了我们的方法，并报告他们在比赛中的相对表现。奇怪的是，启发式方法比计划/学习方法强。但是，经验丰富的人类球员可以轻松地赢得大多数比赛与启发式计划AIS的比赛。因此，我们推测俄罗斯方块链接比预期的要困难。我们向社区提供了发现，以改进。
Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper focuses on a new game, Tetris Link, a board game that is still lacking any scientific analysis. Tetris Link has a large branching factor, hampering a traditional heuristic planning approach. We explore heuristic planning and two other approaches: Reinforcement Learning, Monte Carlo tree search. We document our approach and report on their relative performance in a tournament. Curiously, the heuristic approach is stronger than the planning/learning approaches. However, experienced human players easily win the majority of the matches against the heuristic planning AIs. We, therefore, surmise that Tetris Link is more difficult than expected. We offer our findings to the community as a challenge to improve upon.
