Adaptive Singleton-Based Consistencies
Authors: Amine Balafrej, Christian Bessiere, El Houssine Bouyakhf, Gilles Trombettoni
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that adaptive Partitionone-AC can obtain significant speed-ups over arc consistency and over the full version of partition-one-AC. |
| Researcher Affiliation | Academia | Amine Balafrej CNRS, U. Montpellier, France U. Mohammed V Agdal, Morocco Christian Bessiere CNRS, U. Montpellier France El Houssine Bouyakhf FSR, U. Mohammed V Agdal Morocco Gilles Trombettoni CNRS, U. Montpellier France |
| Pseudocode | Yes | Algorithm 1: POAC1(X, D, C) ... Algorithm 2: var POAC(xi, X, D, C, CHANGE) ... Algorithm 3: Test AC(X, D, C {xi = vi}) ... Algorithm 4: Restore Domains(L, UPDATE) |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. It only states 'All the algorithms are implemented in our JAVA CSP solver.' |
| Open Datasets | Yes | We compare these search algorithms on problems available from Lecoutre s webpage.1 ... 1www.cril.univ-artois.fr/ lecoutre/benchmarks.html |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper states 'All the algorithms are implemented in our JAVA CSP solver' but does not specify the Java version or any specific library names with version numbers. |
| Experiment Setup | Yes | The APOAC approach alternates between two phases during search: a short learning phase and a longer exploitation phase. ... The total length of a pair of sequences learning + exploitation is fixed to the parameter LE. ... We initialize max K to max(2 ki 1, 2). ... If ki(j) is close to max K, that is, greater than 3/4max K, we increase max K by 20%. If ki(j) is less than 1/2max K, we reduce max K by 20%. |