---
title: "Chess_db: A Logic Programming Framework for Large Chess Game Datasets"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.21195v1"
permalink: "https://feed.astralor.com/items/itm_56b8b9f274682a9cd6227ff6"
published: "2026-07-23T11:17:41.000Z"
score: 74
channel: "tech"
tags: ["Chess_db","LMDB","PGN","Chess","Logic Programming","Key-Value Store"]
---

# Chess_db: A Logic Programming Framework for Large Chess Game Datasets

## AI commentary

> This paper, though focused on chess, offers a valuable pattern for handling large structured datasets with logic programming and key-value stores. If you're building systems that need fast historical queries (e.g., game logs, track records, event sequences), Chess_db's architecture is transferable. The code quality is good, recommended for engineers interested in data-intensive AI applications or chess AI.

## At a glance

Chess_db is a suite of logic programming tools designed to efficiently manipulate large-scale chess game datasets. It supports creating databases from PGN files and leverages open-source key-value stores for near-instant position queries, including historical games of specific players and victory statistics for each side. This work challenges the assumption that engine supremacy diminishes interest in chess, focusing instead on computational resources for player training. Experiments show the framework handles millions of games with rapid retrieval of key statistics, offering practical value for chess analysis and AI research.

## Key points

- Proposes Chess_db framework using logic programming (e.g., Prolog) to handle board positions and game data.
- Supports creating databases from PGN files and uses open-source key-value stores (e.g., LMDB) for fast position queries.
- Enables queries on specific player's game history and win statistics for both colors at given positions.
- Open-source implementation suitable for chess training, engine data analysis, etc.

## Full text

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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