The Expressive Power of Ad-Hoc Constraints for Modelling CSPs
Authors: Ruiwei Wang, Roland H.C. Yap
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use a large set of constraint families to investigate the expressive power of 14 existing ad-hoc constraints. We show a complete map of the succinctness of the ad-hoc constraints. We also present results on the tractability of applying various operations and queries on the ad-hoc constraints. Finally, we give case studies illustrating how our results can be useful for questions in the modelling of CSPs. |
| Researcher Affiliation | Academia | School of Computing, National University of Singapore, 13 Computing Drive, 117417, Singapore {ruiwei,ryap}@comp.nus.edu.sg |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-source code availability or links to repositories for the described methodology. |
| Open Datasets | No | The paper uses "15 families of constraints" as counterexamples and formal definitions (Table 2), not empirical datasets in the traditional sense. It does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for publicly available datasets. |
| Dataset Splits | No | The paper does not describe any training, validation, or test dataset splits as it analyzes properties of constraint families rather than running experiments on empirical data. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its investigations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, hyperparameters, or training configurations, as it is a theoretical analysis of constraint properties rather than an empirical study with models. |