CA-CDR

Migrated to https://github.com/manleviet/CA-CDR-V2

A Maven package for Consistency-based Algorithms for Conflict Detection and Resolution (CA-CDR).

Conflict Detection and Resolution is a substantial task in Knowledge Base Engineering (KBE). Intelligent mechanisms are urgently needed, especially in large-scale knowledge bases. This repository publishes our implementations for some/our consistency-based algorithms, which can be utilized in all three phases of KBE, i.e., design, testing and debugging, and configuration.

If you use my implementations in your research, please cite the papers listed in the References.

List of algorithms:

1. QuickXPlain [1]
2. FastDiag [2]
3. MSS-based FastDiag
4. FlexDiag [3]
5. HS-tree [8]
6. HSDAG [9]
7. (coming soon) DirectDebug [4, 5, 6, 7] (the related papers and an executable evaluation on CodeOcean published in March and June 2021)
8. (coming soon) KBDiag (the related paper submitted in January 2022)
9. (coming soon) WipeOutR_T (the related paper submitted in October 2021)
10. (coming soon) WipeOutR_FM (the related paper submitted in October 2021)
11. (coming soon) AggregatedTest (the related paper submitted in November 2021)
12. (coming soon) InformedQX
13. (coming soon) FastDiagP

Examples

There are some test models in here and some examples, showing how to use these algorithms, in here.

References

1. U. Junker. 2004. QuickXPlain: preferred explanations and relaxations for over-constrained problems. In Proceedings of the 19th national conference on Artificial intelligence (AAAI'04). AAAI Press, 167–172. https://dl.acm.org/doi/abs/10.5555/1597148.1597177
2. A. Felfernig, M. Schubert, and C. Zehentner. 2012. An efficient diagnosis algorithm for inconsistent constraint sets. Artif. Intell. Eng. Des. Anal. Manuf. 26, 1 (February 2012), 53–62. DOI:https://doi.org/10.1017/S0890060411000011
3. Felfernig, A., Walter, R., Galindo, J.A. et al. Anytime diagnosis for reconfiguration. J Intell Inf Syst 51, 161–182 (2018). https://doi.org/10.1007/s10844-017-0492-1
4. V.M. Le, A. Felfernig, M. Uta, D. Benavides, J. Galindo, and T.N.T. Tran, DIRECTDEBUG: Automated Testing and Debugging of Feature Models, 2021 IEEE/ACM 43rd International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), 2021, pp. 81-85, doi: https://doi.org/10.1109/ICSE-NIER52604.2021.00025.
5. V.M. Le, A. Felfernig, T.N.T. Tran, M. Atas, M. Uta, D. Benavides, J. Galindo, DirectDebug: A software package for the automated testing and debugging of feature models, Software Impacts, Volume 9, 2021, 100085, ISSN 2665-9638, https://doi.org/10.1016/j.simpa.2021.100085.
6. DirectDebug's Original version with an evaluation in https://github.com/AIG-ist-tugraz/DirectDebug.
7. An executable evaluation of DirectDebug on CodeOcean https://codeocean.com/capsule/5824065/tree/v1
8. R. Reiter, A theory of diagnosis from first principles, Artificial Intelligence, Volume 32, Issue 1, 1987, pp. 57-95, ISSN 0004-3702, https://doi.org/10.1016/0004-3702(87)90062-2.
9. R. Greiner, B. A. Smith, and R. W. Wilkerson, A correction to the algorithm in reiter’s theory of diagnosis, Artif Intell, vol. 41, no. 1, pp. 79–88, 1989, https://doi.org/10.1016/0004-3702(89)90079-9.