A Propagator Design Framework for Constraints over Sequences
Authors: Jean-Noel Monette, Pierre Flener, Justin Pearson
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, for LONGESTPLATEAU, we compare the pruning strengths (independently of search) of these representations with the best that can be achieved without tuple variables, corresponding to ensuring domain consistency (DC) of each Ci with an E3 representation. For several illustrative combinations of n and d, we randomly sample all possible domains for the Xi and L variables. The results are reported in Table 1, by giving the average reduction of the product of the domain sizes with respect to the maximum possible reduction (obtained by global domain consistency). |
| Researcher Affiliation | Academia | Jean-No el Monette and Pierre Flener and Justin Pearson Uppsala University, Dept of Information Technology 751 05 Uppsala, Sweden First Name.Last Name@it.uu.se |
| Pseudocode | No | The paper describes pruning rules and transformations using mathematical notation and functional expressions (e.g., 'smap o filter'), but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions supplementary material at a URL but does not explicitly state that the source code for their proposed methodology or framework is available. The supplemental material is for |
| Open Datasets | No | For several illustrative combinations of n and d, we randomly sample all possible domains for the Xi and L variables. The paper states that data was randomly sampled, implying it was generated by the authors, and does not provide concrete access information (link, DOI, citation) to a publicly available dataset. |
| Dataset Splits | No | The paper mentions 'randomly sample all possible domains' but does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the approach for comparing propagators and their complexities, but it does not provide specific hyperparameters, training configurations, or system-level settings (e.g., learning rate, batch size, optimizer settings) as commonly found in experimental setup sections. |