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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Challenging state-of-the-art move ordering with Adaptive Data Structures
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Challenging state-of-the-art move ordering with Adaptive Data Structures

机译：用自适应数据结构挑战最先进的移动订购

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摘要

The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well-known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, i.e., by searching the best element first. A wide range of techniques have been developed over the years to achieve good move ordering, and improved tree pruning, in the field, in general and in particular, in the alpha-beta search, have been extensively studied. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we had previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion. Our previous results confirmed that it performs well in a very wide range of cases, and in varied types of board games. However, our previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, namely, the History Heuristic, and the Killer Moves technique. We also introduce, in this work, a mechanism by which these established move ordering strategies can be approximated, or directly implemented, in terms of ADSs. We confirm that, in a wide range of two-player and multi-player games, at various points in the game’s progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.
机译：游戏领域在AI的背景下是一个特别高的地区，导致强大的技术的发展，例如alpha-β搜索，能够实现对抗聪明对手的竞争游戏。众所周知，树修剪策略，如alpha-beta，从适当的移动排序中强烈地利益，即首先搜索最佳元素。多年来，多年来已经开发了广泛的技术，以实现良好的举动排序，并且在该领域的完善的树修剪，通常，在alpha-beta搜索中，已经广泛研究。灵感来自前手中的自适应数据结构（ADSS）的不相关领域，我们之前介绍过历史广告技术，该技术采用自适应列表以实现有效和动态的移动顺序，以域独立方式。我们以前的结果证实它在非常广泛的案例中表现良好，以及各种各样的棋盘游戏。但是，我们以前的工作没有比较历史广告启发式的历史ads启发式的表现，以任何既定的行动订购策略。为了解决这个问题，我们在这里展示了与两个众所周知的伟大的策略相比，这些策略在历史广告的类似哲学中运作，即历史启发式，杀手移动技术。在这项工作中，我们还介绍了这些已建立的移动订购策略可以近似或直接实施的机制，或直接在广告方面进行。我们确认，在各种两位玩家和多人游戏中，在游戏进展的各个点，历史广告至少以及这些策略，实际上，在大多数中表现出它们的优势案件。

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