Stochastic Constraint Programming with And-Or Branch-and-Bound

Authors: Behrouz Babaki, Tias Guns, Luc de Raedt

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 efficiency 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.