Learning Robust Search Strategies Using a Bandit-Based Approach
Authors: Wei Xia, Roland Yap
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary experiments show that our adaptive technique is more robust than the original search heuristics. |
| Researcher Affiliation | Academia | Wei Xia, Roland H. C. Yap School of Computing, National University of Singapore, Singapore {xiawei,ryap}@comp.nus.edu.sg |
| Pseudocode | Yes | Algorithm 1: Thompson Sampling |
| Open Source Code | No | The paper mentions the use of "Abs Con solver (https://www.cril.univ-artois. fr/ lecoutre/software.html)" but does not provide any link or statement indicating that the authors' own implementation code is open-source or publicly available. |
| Open Datasets | Yes | The benchmarks are from the CSP solver competition (http://www.cril.univ-artois. fr/CSC09). |
| Dataset Splits | No | The paper evaluates its methods on 363 problem instances from 15 problem series and discusses runtime ratios, but it does not specify explicit training, validation, or test dataset splits typical for machine learning experiments. |
| Hardware Specification | Yes | The experiments were run on a 3.40GHz Intel i7-4770 machine. |
| Software Dependencies | No | The paper states: "We use the Abs Con solver (https://www.cril.univ-artois. fr/ lecoutre/software.html)" but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | CPU time is limited to 1800 seconds per instance and memory to 8GB. ... We employ a full initialization of variable impact and activity at the root node of the search tree using the singleton arc consistency ... We use the same lexicographic value heuristic (lexico) for all cases. |