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Model-Based Diagnosis with Multiple Observations

Ignatiev, Alexey, Morgado, Antonio, Weissenbacher, Georg, Marques-Silva, Joao·2019·Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
DOI 10.24963/ijcai.2019/155
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Abstract

Existing automated testing frameworks require multiple observations to be jointly diagnosed with the purpose of identifying common fault locations. This is the case for example with continuous integration tools. This paper shows that existing solutions fail to compute the set of minimal diagnoses, and as a result run times can increase by orders of magnitude. The paper proposes not only solutions to correct existing algorithms, but also conditions for improving their run times. Nevertheless, the diagnosis of multiple observations raises a number of important computational challenges, which even the corrected algorithms are often unable to cope with. As a result, the paper devises a novel algorithm for diagnosing multiple observations, which is shown to enable significant performance improvements in practice.

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Research works printed but not linked: 40

  1. L. Ardissono, L. Console, A. Goy, G. Petrone, C. Picardi, M. Segnan, and D. T. Dupré. Co- operative model-based diagnosis of web services. In DX, pages 125-130, 2005.
  2. M. F. Arif, C. Mencía, and J. Marques- Silva. Efficient MUS enumeration of Horn formulae with applications to axiom pinpointing. In SAT, pages 324-342, 2015.
  3. G. Audemard, J. Lagniez, and L. Si- mon. Improving Glucose for incremental SAT solving with assumptions: Application to MUS extraction. In SAT, pages 309-317, 2013.
  4. J. Bailey and P. J. Stuckey. Dis- covery of minimal unsatisfiable subsets of constraints us- ing hitting set dualization. In PADL, pages 174-186, 2005.
  5. E. Birnbaum and E. L. Lozinskii. Consistent subsets of inconsistent systems: structure and behaviour. J. Exp. Theor. Artif. Intell., 15(1):25-46, 2003.
  6. F. Brglez and H. Fujiwara. A neutral list of 10 combinational benchmark circuits and a target translator in FORTRAN. In ISCAS, pages 695-698, Jun. 1985.
  7. K. Chandrasekaran, R. M. Karp, E. Moreno-Centeno, and S. Vempala. Algorithms for implicit hitting set problems. In SODA, pages 614- 629, 2011.
  8. J. Davies and F. Bacchus. Solv- ing MAXSAT by solving a sequence of simpler SAT in- stances. In CP, pages 225-239, 2011.
  9. J. de Kleer and B. C. Williams. Diagnosing multiple faults. Artif. Intell., 32(1):97-130, 1987.
  10. J. de Kleer. An improved approach for gen- erating max-fault min-cardinality diagnoses. In DX, pages 247-252, 2008.
  11. A. Feldman, G. M. Provan, and A. J. C. van Gemund. Solving strong-fault diagnostic mod- els by model relaxation. In IJCAI, pages 785-790, 2009.
  12. Feldman et al., 2010a] A. Feldman, G. Provan, J. de Kleer, S. Robert, and A. van Gemund. Solving model-based di- agnosis problems with Max-SAT solvers and vice versa. In DX, pages 185-192, 2010.
  13. É. Grégoire, Y. Izza, and J. Lagniez. Boosting MCSes enumeration. In IJCAI, pages 1309- 1315, 2018.
  14. Huang and Darwiche, 2005] J. Huang and A. Darwiche. On compiling system models for faster and more scalable di- agnosis. In AAAI, pages 300-306, 2005.
  15. A. Ignatiev, A. Previti, M. H. Liffiton, and J. Marques-Silva. Smallest MUS extraction with min- imal hitting set dualization. In CP, pages 173-182, 2015.
  16. Ignatiev et al., 2016a] A. Ignatiev, A. Morgado, and J. Marques-Silva. Propositional abduction with implicit hitting sets. In ECAI, pages 1327-1335, 2016.
  17. Ignatiev et al., 2016b] A. Ignatiev, A. Previti, and J. Marques-Silva. On finding minimum satisfying assignments. In CP, pages 287-297, 2016.
  18. A. Ignatiev, A. Morgado, and J. Marques-Silva. Model based diagnosis of multi- ple observations with implicit hitting sets. CoRR, abs/1707.01972, 2017. · arXiv
  19. A. Ignatiev, A. Morgado, and J. Marques-Silva. PySAT: A Python toolkit for pro- totyping with SAT oracles. In SAT, pages 428-437, 2018.
  20. D. Jannach and T. Schmitz. Model-based diagnosis of spreadsheet programs: a constraint-based debugging approach. Autom. Softw. Eng., 23(1):105-144, 2016.
  21. M. Jose and R. Majumdar. Cause clue clauses: error localization using maximum sat- isfiability. In PLDI, pages 437-446, 2011.
  22. S. Lamraoui and S. Naka- jima. A formula-based approach for automatic fault lo- calization of imperative programs. In ICFEM, pages 251- 266, 2014.
  23. S. Lamraoui and S. Naka- jima. A formula-based approach for automatic fault local- ization of multi-fault programs. JIP, 24(1):88-98, 2016.
  24. M. H. Liffiton and K. A. Sakallah. Algorithms for computing minimal unsatisfiable subsets of constraints. J. Autom. Reasoning, 40(1):1-33, 2008.
  25. M. H. Liffiton, A. Previti, A. Malik, and J. Marques-Silva. Fast, flexible MUS enumeration. Constraints, 21(2):223-250, 2016.
  26. Marques-Silva et al., 2015] J. Marques-Silva, M. Janota, A. Ignatiev, and A. Morgado. Efficient model based diag- nosis with maximum satisfiability. In IJCAI, pages 1966- 1972, 2015.
  27. C. Mencía, A. Previti, and J. Marques- Silva. Literal-based MCS extraction. In IJCAI, pages 1973-1979, 2015.
  28. A. Morgado, C. Dodaro, and J. Marques-Silva. Core-guided MaxSAT with soft cardinality constraints. In CP, pages 564-573, 2014.
  29. Nica et al., 2013] I. Nica, I. Pill, T. Quaritsch, and F. Wotawa. The route to success -a performance com- parison of diagnosis algorithms. In IJCAI, 2013.
  30. Pietersma and van Gemund, 2006] J. Pietersma and A. J. C. van Gemund. Temporal versus spatial observability in model-based diagnosis. In ICSMC, pages 5325-5331, 2006.
  31. A. Previti, A. Ignatiev, A. Morgado, and J. Marques-Silva. Prime compilation of non-clausal for- mulae. In IJCAI, pages 1980-1988, 2015.
  32. A. Previti, C. Mencía, M. Järvisalo, and J. Marques-Silva. Premise set caching for enumerating minimal correction subsets. In AAAI, pages 6633-6640, 2018.
  33. R. Reiter. A theory of diagnosis from first principles. Artif. Intell., 32(1):57-95, 1987.
  34. S. Safarpour, H. Mangassarian, A. G. Veneris, M. H. Liffiton, and K. A. Sakallah. Im- proved design debugging using maximum satisfiability. In FMCAD, pages 13-19, 2007.
  35. P. Saikko, J. Berg, and M. Järvisalo. LMHS: A SAT-IP hybrid MaxSAT solver. In SAT, pages 539-546, 2016.
  36. S. Schlobach, Z. Huang, R. Cornet, and F. van Harmelen. Debugging incoherent terminolo- gies. J. Autom. Reasoning, 39(3):317-349, 2007.
  37. S. A. Siddiqi. Computing minimum- cardinality diagnoses by model relaxation. In IJCAI, pages 1087-1092, 2011.
  38. R. T. Stern, M. Kalech, A. Feldman, and G. M. Provan. Exploring the duality in conflict-directed model-based diagnosis. In AAAI, 2012.
  39. P. J. Stuckey, M. Sulzmann, and J. Wazny. Interactive type debugging in Haskell. In Haskell, pages 72-83, 2003.
  40. E. Torlak, F. S. Chang, and D. Jackson. Finding minimal unsatisfiable cores of declarative specifi- cations. In FM, pages 326-341, 2008.

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Metadata

Authors
Ignatiev, A., Morgado, A., Weissenbacher, G., & Marques-Silva, J.
Affiliations
  • Faculty of Science, University of Lisbon, Portugal
  • TU Wien, Vienna, Austria
Venue
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence Organization
Published
2019-08
Pages
1108-1115
DOI
10.24963/ijcai.2019/155
Cited by
15
References
0

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CoChat. (2026). Citation check: Model-Based Diagnosis with Multiple Observations. https://app.cochat.ai/papers/10.24963/ijcai.2019/155
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Ignatiev, A., Morgado, A., Weissenbacher, G., & Marques-Silva, J. (2019). Model-Based Diagnosis with Multiple Observations. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/155

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.