Compiling Constraint Networks into Multivalued Decomposable Decision Graphs
Authors: Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, Samuel Thomas
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experiments showed that our compiler cn2mddg succeeds in compiling CNs which are out of the reach of standard approaches based on a translation of the input network to CNF, followed by a compilation to Decision-DNNF. |
| Researcher Affiliation | Academia | Fr ed eric Koriche, Jean-Marie Lagniez, Pierre Marquis, Samuel Thomas CRIL-CNRS and Universit e d Artois, Lens, France {koriche, lagniez, marquis, thomas}@cril.univ-artois.fr |
| Pseudocode | Yes | Algorithm 1 provides the pseudo-code for the compiler cn2mddg. |
| Open Source Code | Yes | The run-time code of our compiler, as well as the translators and the benchmarks used in our experiments, and additional empirical results, can be downloaded from www.cril.fr/KC/ |
| Open Datasets | Yes | We have considered 173 CNs from 15 data sets, downloaded from github.com/Mini Zinc/minizincbenchmarks, www.cril.univ-artois.fr/ lecoutre/benchmarks.html, and www.itu.dk/research/cla/externals/clib/. |
| Dataset Splits | No | The paper uses |
| Hardware Specification | Yes | Our experiments have been conducted on a Quadcore Intel XEON X5550 with 32GB of memory. |
| Software Dependencies | No | The paper mentions software components and formats like 'XCSP 2.1 format', 'c2d', 'Dsharp', 'Sugar', and 'Azucar', but it does not provide specific version numbers for these or other software dependencies required to reproduce the experiments. |
| Experiment Setup | No | The paper describes the techniques and heuristics used in their compiler (e.g., caching, variable ordering based on betweenness centrality, universal constraint handling) but does not provide specific hyperparameter values or concrete system-level training/experimental settings typical for reproducibility (e.g., learning rates, batch sizes, epochs). |