AGENTIC-SYSTEMSMay 27, 20261 min read11 views

Constraint acquisition needs better benchmarks

Research on Constraint Acquisition (CA) in mathematical programming is hindered by inadequate benchmarks. To address this, MPMMine has been introduced as a standardized benchmark suite to improve consistency, reproducibility, and support for next-generation AI algorithms.

Computer Science > Artificial Intelligence

Title:Constraint acquisition needs better benchmarks

View PDF HTML (experimental)Abstract: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.

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Source: arXiv cs.AI Recent

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