Challenging Established Move Ordering Strategies with Adaptive Data Structures

Abstract

Abstract. 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, that is, searching the best element first. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we have previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion, and found that it performs well in a wide range of cases. However, 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, the History Heuristic, and the Killer Moves technique. We find 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.

Enhancing AI-Based Game Playing Using Adaptive Data Structures

“…The specific contributions presented in each chapter of the thesis are summarized in the following section. The publications that have resulted from these works are mentioned in each respective chapter (the specific publications related to this thesis are: [37], [38], [39], [40], [41], [42], [43], and [44]).…”

“…The History Heuristic, described in Chapter 2, employs a more complex mechanism 1 The work in this chapter was originally published in [42] to rank moves. It is, however, less sensitive to change than the Move-to-Front rule.…”