Computational Intelligence and Computer Search

计算智能和计算机搜索

• 批准号: RGPIN-2016-04601
• 负责人: Hayward, Ryan
• 金额: $ 1.6万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615174
• 关键词: Computational; Intelligence; Computer; Search

For centuries, games such as chess and Go have served as an arena in which humans showed their intellectual strength. Today, such games serve as an arena in which machines show their computational intelligence. A key aspect of computational intelligence is search. The goal of this project is to find improved search algorithms. A typical problem is to find the next move in a two-player game such as chess or Go. Here, the algorithm must search a large space of possible continuations of the game and select a move which will turn out to be strong. Historically, the problem of building strong artificial players for games such as chess or Go has been a main challenge in the field of artificial intelligence. Recently, there has been tremendous progress in this field. Around 10 years ago, a new search algorithm --- Monte Carlo Tree Search (MCTS) --- that combines classical tree-search techniques with random move sampling revolutionized computer Go. I plan to continue work on fundamental problems that both explain the success of MCTS and lead to improvements for it. More recently, another algorithm --- Deep Learning --- has emerged that promises another such revolution. I plan to work on this as well.

几个世纪以来，国际象棋和围棋等游戏一直是人类展示智力力量的竞技场。如今，这类游戏成了机器展示其计算智能的舞台。计算智能的一个关键方面是搜索。 这个项目的目标是找到改进的搜索算法。一个典型的问题是在两人博弈中寻找下一步棋，比如国际象棋或围棋。在这里，算法必须搜索大空间的可能的游戏继续，并选择一个将被证明是强大的走法。从历史上看，为国际象棋或围棋等游戏打造强大的人工棋手的问题一直是人工智能领域的主要挑战。 近年来，这一领域取得了巨大的进展。大约10年前，一种新的搜索算法-蒙特卡罗树搜索(MCTS)-将经典的树搜索技术与随机移动抽样相结合，使计算机围棋发生了革命性的变化。我计划继续在基本问题上工作，这些问题既可以解释MCTS的成功，也可以导致对其进行改进。最近，另一种算法-深度学习-出现了，它承诺会发生另一场这样的革命。我也计划在这方面下功夫。