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..
Stochastic Constraint Programming with And-Or Branch-and-Bound
Authors: Behrouz Babaki, Tias Guns, Luc de Raedt
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We investigate the following questions: Q1: Does our proposed method improve over existing approaches? Q2: What is the impact of bounding depth on the ef๏ฌciency of search? Q3: What is the interplay between bounding and constraint propagation? We used two problems in our experiments: Knapsack (based on an example from [Hnich et al., 2011]) [...] Investment [...] Table 1 shows the results. [...] Figure 2 shows that even the shallow bound is much better than no bound. [...] Figure 3 shows that in the absence of bounds, tightening the constraint leads to more failures and fewer nodes; meaning that the search space becomes smaller. |
| Researcher Affiliation | Academia | 1Department of Computer Science, KU Leuven, Belgium 2Department of Business Technology and Operations, VUB, Belgium |
| Pseudocode | Yes | Algorithm 1 And-Or search over domain D following |
| Open Source Code | Yes | The code and data are available online 3. 3https://github.com/Behrouz-Babaki/Factored SCP |
| Open Datasets | No | We used two problems in our experiments: Knapsack (based on an example from [Hnich et al., 2011]) [...] Other distribution parameters for both problems were generated randomly |
| Dataset Splits | No | The paper describes problems like Knapsack and Investment and their setup, but does not provide details on specific training, validation, or test dataset splits. |
| Hardware Specification | No | We ran the experiments on Linux machines with 32 GB of memory. |
| Software Dependencies | Yes | The CP solver used is Gecode-4.4.0 and the MIP solver is Gurobi-6.51. We used the the ACE2 package to compile the Bayesian networks into arithmetic circuits, and ported the inference library to C++ for integration with Gecode. |
| Experiment Setup | No | The time-out used was 1800 seconds. |