Multi-Armed Bandits for Adaptive Constraint Propagation
Authors: Amine Balafrej, Christian Bessiere, Anastasia Paparrizou
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation demonstrates that the proposed technique results in a more efficient and stable solver. |
| Researcher Affiliation | Academia | Amine Balafrej TASC (INRIA/CNRS), Mines Nantes Nantes, France amine.balafrej@mines-nantes.fr Christian Bessiere CNRS, U. Montpellier Montpellier, France bessiere@lirmm.fr Anastasia Paparrizou CNRS, U. Montpellier Montpellier, France paparrizou@lirmm.fr |
| Pseudocode | No | The paper describes the steps of the algorithm in paragraph text (Section 4.2) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any link or explicit statement about the release of its source code. |
| Open Datasets | Yes | We ran experiments on problem classes from real world applications and classes following a regular pattern involving a random generation (REAL and PATT in www.cril.univ-artois.fr/ lecoutre/benchmarks.html). |
| Dataset Splits | No | The paper focuses on online learning and does not specify traditional train/validation/test dataset splits with percentages or counts. It states that the MAB framework 'does not require any training or information from preprocessing'. |
| Hardware Specification | Yes | The algorithms were implemented with a CP solver written in Java and tested on an 2.8 GHz Intel Xeon processor and 16 GB RAM. |
| Software Dependencies | No | The paper mentions 'a CP solver written in Java' and specific consistency algorithms by name (AC2001, max RPC3, POAC1) along with their citations, but it does not provide specific version numbers for Java or any of the mentioned software components. |
| Experiment Setup | Yes | A cut-off of 3,600 seconds was set for all algorithms and all instances. We used the dom/deg heuristic for variable ordering and lexicographic value ordering. |