Constraint acquisition needs better benchmarks
The paper discusses the need for improved benchmarks in Constraint Acquisition (CA) research. Current benchmarks are inadequate, affecting the reproducibility and comparability of CA methods. The authors introduce MPMMine, a new benchmark suite designed to enhance the assessment of algorithms in this field.
- ▪Constraint Acquisition research is hindered by inadequate benchmarks.
- ▪Existing benchmarks are primarily for solver evaluation and lack consistency.
- ▪MPMMine is a new benchmark suite that provides a standardized approach for assessing CA algorithms.
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Computer Science > Artificial Intelligence arXiv:2605.26279 (cs) [Submitted on 25 May 2026] Title:Constraint acquisition needs better benchmarks Authors:Rafał Stachowiak, Tomasz P. Pawlak View a PDF of the paper titled Constraint acquisition needs better benchmarks, by Rafa{\l} Stachowiak and 1 other authors 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.
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