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..
Efficient Path Consistency Algorithm for Large Qualitative Constraint Networks
Authors: Zhiguo Long, Michael Sioutis, Sanjiang Li
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results with both real-world and synthetic datasets also confirm in practice that DPC+ can be significantly more efficient than PPC (and PC). and 5 Experimental Results We evaluate the performance of our implementation of the DPC+ algorithm, against an implementation of the state-of-the-art PPC enforcing algorithm (PPC), for QCNs that are defined over a distributive subalgebra. |
| Researcher Affiliation | Academia | Zhiguo Long QCIS, FEIT University of Technology Sydney Australia EMAIL Michael Sioutis CRIL-CNRS UMR 8188 University of Artois France EMAIL Sanjiang Li UTS-AMSS Joint Lab QCIS, FEIT University of Technology Sydney Australia EMAIL |
| Pseudocode | Yes | Algorithm 1: DPC(N, ) and Algorithm 2: DPC+(N, ) |
| Open Source Code | No | The paper mentions that 'All algorithms were coded in Python and run with Py Py 2.2.1 [Py Py Team, 2015], which implements Python 2.7.' but does not provide a link or explicit statement about the availability of *their* specific implementation code. |
| Open Datasets | Yes | We considered random RCC8 networks generated by the BA(n, m) model [Barabasi and Albert, 1999]... Regarding real-world RCC8 datasets, we employed the ones recently used in [Sioutis et al., 2015b; Li et al., 2015], viz., nuts (nomenclature of territorial units)2... adm1 (administrative geography of Great Britain) [Goodwin et al., 2008]... gadm1 (German administrative units)2... gadm2 (the world s administrative areas) [Geo Vocab, 2012]... adm2 (the administrative geography of Greece)2... fprints (geographic footprints in the Southampton area of the UK) [Li et al., 2015]... and stareas (statistical areas in Tasmania) [Li et al., 2015]. and 2Retrieved from: http://www.linkedopendata.gr/ |
| Dataset Splits | No | The paper provides details about the datasets used but does not specify any training, validation, or test splits, nor does it mention cross-validation or stratified splitting. |
| Hardware Specification | Yes | The experimentation was carried out on a computer with an Intel Core i7-2820QM processor with a 2.30 GHz frequency per CPU core, 8 GB of RAM, and the Trusty Tahr x86 64 OS. |
| Software Dependencies | Yes | All algorithms were coded in Python and run with Py Py 2.2.1 [Py Py Team, 2015], which implements Python 2.7. |
| Experiment Setup | Yes | We considered 10 satisfiable RCC8 networks of model BA(n, m) for each order 1000 n 10000 of their constraint graphs with a 1000-vertex step and a preferential attachment value of m = 2. The edges of these graphs were labelled with relations from the maximal distributive subclass D64 8 of RCC8 [Li et al., 2015]. and The maximum cardinality search algorithm [Tarjan and Yannakakis, 1984] was used to obtain a variable elimination ordering for DPC and DPC+, and a triangulation of the constraint graph of a given QCN for PPC as described in [Sioutis et al., 2015a]. |