为数学规划模型构建获取提供标准化新基准，解决方法难复现问题
Constraint acquisition needs better benchmarks
- 设计统一结构的MPMMine基准，支持多种领域知识输入
- 每问题含多模型、数百实例，覆盖整数与连续域解空间
- 适合开发和评测自动建模算法的研究者使用
约束获取（CA）及其相关研究在从领域知识中验证与增强数学规划（MP）模型方面，受限于现有基准的不足。当前基准多用于求解器评估，组织松散、处理不一致，且缺少CA方法所需领域知识文档。本文提出MPMMine基准套件，专为评估利用多样化领域知识发现、验证和优化MP模型的算法而设计。其遵循一致性、标准化、完整性、可扩展性、开放性和版本控制原则，采用MiniZinc、CommonMark和JSON等开放格式，每个问题提供多个模型、每模型数十至数百实例，涵盖数千个解与非解，覆盖整数与连续变量域，并配有自然语言描述，支持文本到模型方法研究。
原文摘要 · Abstract (English)
Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models from domain knowledge artifacts are currently limited by inadequate benchmarks. This deficiency impedes reproducibility and cross-study comparability, slowing the maturation of CA methods. Existing benchmarks were designed for solver evaluation rather than for assessing CA algorithms. They are loosely organized, treat individual problems inconsistently, and omit the domain knowledge artifacts required by CA methods. This work presents MPMMine, a benchmark suite designed to assess algorithms that discover, validate, and enhance MP models using diverse domain knowledge artifacts. MPMMine is guided by consistency, standardization, completeness, extensibility, openness, and version control. It adopts a uniform structure and relies on open formats: MiniZinc, CommonMark, and JSON. It provides multiple models per problem, tens of instances per model, and thousands of solutions and non-solutions in both integer and continuous domains, alongside natural-language descriptions to support text-to-model methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。