INFORMEDQX: Informed Conflict Detection for Over-Constrained Problems
Authors: Viet-Man Le, Alexander Felfernig, Thi Ngoc Trang Tran, Mathias Uta
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of a related performance analysis with the Linux2.6.3.33 configuration knowledge base show significant improvements in terms of runtime performance compared to QUICKXPLAIN. On the basis of an evaluation with a real-world configuration knowledge base, we show a significantly improved conflict detection performance compared to the basic QUICKXPLAIN algorithm. |
| Researcher Affiliation | Collaboration | 1 Graz University of Technology, Graz, Austria 2 Siemens Energy AG, Germany 3 University of Economics, Hue University, Hue, Vietnam 4 School of Hospitality and Tourism, Hue University, Hue, Vietnam {vietman.le,alexander.felfernig,ttrang}@ist.tugraz.at, mathias.uta@siemens-energy.com |
| Pseudocode | Yes | Algorithm 1: QUICKXPLAIN(CR, CKB) : CS, Algorithm 2: QX( , CR = {c1 . . cn}, CKB) : CS, Algorithm 3: INFORMEDQX(CR, CKB, N) : CS |
| Open Source Code | Yes | The dataset, the implementation of algorithms, and evaluation programs can be found at https://github.com/AIG-isttugraz/Informed QX. |
| Open Datasets | Yes | We have evaluated the performance of INFORMEDQX compared to QUICKXPLAIN on the basis of the Linux-2.6.33.3 configuration knowledge base taken from Diverso Lab s benchmark1 (Heradio et al. 2022). The dataset, the implementation of algorithms, and evaluation programs can be found at https://github.com/AIG-isttugraz/Informed QX. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning for model training or validation. |
| Hardware Specification | Yes | All experiments have been conducted with an Apple M1 Pro (8 cores) computer with 16-GB RAM. |
| Software Dependencies | Yes | For evaluation purposes, we used the CHOCO solver3 to perform consistency checks and JAVABDD v6.0.04 to build a BDD of identified conflict sets. |
| Experiment Setup | No | The paper does not contain specific experimental setup details like hyperparameter values (e.g., learning rates, batch sizes), optimizer settings, or explicit training configurations, as it focuses on algorithmic performance rather than machine learning model training. |