构建大型国际象棋数据集的高效处理框架，支持快速查询与分析。
Chess\_db: A framework for working with large chess game datasets
- 基于逻辑编程构建，可内存操作或持久化存储棋局数据。
- 从PGN文件生成数据库，支持对数百万棋局的即时查询。
- 适用于棋手分析、开局研究及引擎训练的数据基础设施。
国际象棋作为经典人工智能领域代表，虽曾因计算机程序超越人类而被认为失去吸引力，但近年来反而迎来复兴。当前计算资源大量投入于棋手训练，而历史棋局数据的重要性日益凸显——不仅需了解特定棋手的对局记录，还需分析特定局面下各颜色方获胜频率。本文提出Chess_db，一套基于逻辑编程的工具框架，能高效处理棋局数据，支持在内存中操作或构建后端数据库。特别地，该框架提供从PGN（可移植游戏记谱法）文件生成数据库的通用代码，并探索开源键值数据库在存储位置表方面的适用性，实现对海量棋局数据的近乎实时访问。
原文摘要 · Abstract (English)
Chess is a two player strategic game that is embedded in classical AI culture as it was once the frontier for intelligent behaviour. There was the silent assumption that the advent of computer engines that play better than the best humans will extinguish interest in the game. However, the opposite has come to pass, with a growing following for the game. A lot of the computational resources are now centered around training of players, where the engine output is just one aspect. Access to past games is also an essential part, both in knowing what games a specific player has played previously, and also which continuations at a certain position have led to victory more often for each of the two colour players. We present Chess_db a suite of logic programming tools that can effectively manipulate games both in memory and via creating back end databases. In particular, we provide versatile code that creates databases from PGN (portable game notation) game files and explore the suitability of open source key-value databases for storing position tables that provide near-instant access to information pertaining to substantially large number of games.
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