Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Ranking Constraints
Authors: Christian Bessiere, Emmanuel Hebrard, George Katsirelos, Zeynep Kiziltan, Toby Walsh
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | report experimental results demonstrating the promise of our proposed approach. 4 Experimental Evaluation |
| Researcher Affiliation | Academia | Christian Bessiere LIRMM-CNRS Montpellier, France; Emmanuel Hebrard LAAS-CNRS Toulouse, France; George Katsirelos INRA Toulouse, France; Zeynep Kiziltan Universit di Bologna Bologna, Italy; Toby Walsh University of New South Wales Sydney, Australia |
| Pseudocode | Yes | Algorithm 1: DCSupport(X1, . . . , Xn); Algorithm 2: RCSupport(X1, . . . , Xn) |
| Open Source Code | No | No statement regarding open-source code availability or links to code repositories was found. |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was found. The paper describes generating its own data instances. |
| Dataset Splits | No | The paper describes generating instances and solving them, but does not provide specific training/validation/test dataset split information. |
| Hardware Specification | Yes | we ran all the experiments on a cluster of AMD opteron 6176 2.3 GHz processors. |
| Software Dependencies | Yes | We used Choco 3 to implement the two propagators |
| Experiment Setup | Yes | We used lexicographic variable ordering and branched on the minimum value in all cases, so the same search tree is explored, modulo pruning. For a number of tasks ranging from n = 5 to 10, we generate 50 such scheduling instances, choosing duration constants pi and demand at random. Then we post a CUMULATIVE constraint, as well as the channeling constraints between overlap and ranking and between ranking and actual durations ripi. |