一种快速计算短15题解的高效内存方法

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-06-01 DOI:10.1109/TCIAIG.2014.2352255

I. Parberry

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引用次数: 8

摘要

虽然15谜题的历史可以追溯到19世纪70年代，但它仍然以移动设备上的应用程序和大型电子游戏中的迷你游戏的形式出现。我们演示了一种解决15谜题的方法，该方法仅使用4.7 MB的表，在一百万个随机实例上能够找到平均65.21步和最坏情况下95步的解决方案，在当前桌面计算硬件上，每个解决方案的时间不到十分之一毫秒。这些数字与最坏情况下的上限80步和1995年发布的贪婪算法相比是有利的，贪婪算法平均需要118步，最坏情况下需要195步。

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A Memory-Efficient Method for Fast Computation of Short 15-Puzzle Solutions

While the 15-puzzle has a long and interesting history dating back to the 1870s, it still continues to appear as apps on mobile devices and as minigames inside larger video games. We demonstrate a method for solving the 15-puzzle using only 4.7 MB of tables that on a million random instances was able to find solutions of 65.21 moves on average and 95 moves in the worst case in under a tenth of a millisecond per solution on current desktop computing hardware. These numbers compare favorably to the worst case upper bound of 80 moves and to the greedy algorithm published in 1995, which required 118 moves on average and 195 moves in the worst case.

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来源期刊

IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

4.60

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.