Skip to main content

Parallel Model-Based Diagnosis

  • Chapter
  • First Online:
  • 1223 Accesses

Abstract

Model-Based Diagnosis (MBD) is a general-purpose computational approach to determine why a system under observation, e.g., an electronic circuit or a software program, does not behave as expected. MBD approaches utilize knowledge about the system’s expected behavior if all of its components work correctly. In case of an unexpected behavior they systematically explore the possible reasons, i.e., diagnoses, for the misbehavior. Such diagnoses are determined through systematic or heuristic search procedures which often use MBD-specific rules to prune the search space. In this chapter we review approaches that rely on parallel or distributed computations to speed up the diagnostic reasoning process. Specifically, we focus on recent parallelization strategies that exploit the capabilities of modern multi-core computer architectures and report results from experimental evaluations to shed light on the speedups that can be achieved by parallelization for various MBD applications.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. de Kleer, J., Mackworth, A.K., Reiter, R.: Characterizing Diagnoses and Systems. Artificial Intelligence 56(2-3) (1992) 197–222

    Google Scholar 

  2. de Kleer, J., Williams, B.C.: Diagnosing Multiple Faults. Artificial Intelligence 32(1) (April 1987) 97–130

    Google Scholar 

  3. Reiter, R.: A Theory of Diagnosis from First Principles. Artificial Intelligence 32(1) (1987) 57–95

    Google Scholar 

  4. Felfernig, A., Friedrich, G., Jannach, D., Stumptner, M.: Consistency-based Diagnosis of Configuration Knowledge Bases. Artificial Intelligence 152(2) (2004) 213–234

    Google Scholar 

  5. Mateis, C., Stumptner, M., Wieland, D., Wotawa, F.: Model-Based Debugging of Java Programs. In: AADEBUG’00. (2000)

    Google Scholar 

  6. Jannach, D., Schmitz, T.: Model-based Diagnosis of Spreadsheet Programs: A Constraint-based Debugging Approach. Automated Software Engineering 23(1) (2016) 105–144

    Google Scholar 

  7. Wotawa, F.: Debugging Hardware Designs Using a Value-Based Model. Applied Intelligence 16(1) (2001) 71–92

    Google Scholar 

  8. Felfernig, A., Friedrich, G., Isak, K., Shchekotykhin, K.M., Teppan, E., Jannach, D.: Automated Debugging of Recommender User Interface Descriptions. Applied Intelligence 31(1) (2009) 1–14

    Google Scholar 

  9. Console, L., Friedrich, G., Dupré, D.T.: Model-Based Diagnosis Meets Error Diagnosis in Logic Programs. In: IJCAI’93. (1993) 1494–1501

    Google Scholar 

  10. Friedrich, G., Shchekotykhin, K.M.: A General Diagnosis Method for Ontologies. In: ISWC’05. (2005) 232–246

    Google Scholar 

  11. Stumptner, M., Wotawa, F.: Debugging Functional Programs. In: IJCAI’99. (1999) 1074–1079

    Google Scholar 

  12. Friedrich, G., Stumptner, M., Wotawa, F.: Model-Based Diagnosis of Hardware Designs. Artificial Intelligence 111(1-2) (1999) 3–39

    Google Scholar 

  13. White, J., Benavides, D., Schmidt, D.C., Trinidad, P., Dougherty, B., Cortés, A.R.: Automated Diagnosis of Feature Model Configurations. Journal of Systems and Software 83(7) (2010) 1094–1107

    Google Scholar 

  14. Friedrich, G., Fugini, M., Mussi, E., Pernici, B., Tagni, G.: Exception Handling for Repair in Service-Based Processes. IEEE Transactions on Software Engineering 36(2) (2010) 198–215

    Google Scholar 

  15. Junker, U.: QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems. In: AAAI’04. (2004) 167–172

    Google Scholar 

  16. Marques-Silva, J., Janota, M., Belov, A.: Minimal Sets over Monotone Predicates in Boolean Formulae. In: Computer Aided Verification. (2013) 592–607

    Google Scholar 

  17. Shchekotykhin, K., Jannach, D., Schmitz, T.: MergeXplain: Fast Computation of Multiple Conflicts for Diagnosis. In: IJCAI’15. (2015) 3221–3228

    Google Scholar 

  18. Greiner, R., Smith, B.,Wilkerson, R.: A Correction to the Algorithm in Reiter’s Theory of Diagnosis. Artificial Intelligence 41(1) (1989) 79–88

    Google Scholar 

  19. Jannach, D., Schmitz, T., Shchekotykhin, K.: Parallel Model-Based Diagnosis On Multi-Core Computers. Journal of Artificial Intelligence Research (JAIR) 55 (2016) 835–887

    Google Scholar 

  20. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co. (1979)

    Google Scholar 

  21. Eiter, T., Gottlob, G.: The Complexity of Logic-Based Abduction. Journal of the ACM 42(1) (1995) 3–42

    Google Scholar 

  22. de Kleer, J.: Hitting Set Algorithms for Model-based Diagnosis. In: DX’11. (2011) 100–105

    Google Scholar 

  23. Stern, R., Kalech, M., Feldman, A., Provan, G.: Exploring the Duality in Conflict-Directed Model-Based Diagnosis. In: AAAI’12. (2012) 828–834

    Google Scholar 

  24. Marques-Silva, J., Janota, M., Ignatiev, A., Morgado, A.: Efficient Model Based Diagnosis with Maximum Satisfiability. In: IJCAI’15. (2015) 1966–1972

    Google Scholar 

  25. de Kleer, J., Williams, B.C.: Diagnosing Multiple Faults. Artif. Intell. 32(1) (apr 1987) 97–130

    Google Scholar 

  26. Williams, B.C., Ragno, R.J.: Conflict-directed A* and its Role in Model-based Embedded Eystems. Discrete Applied Mathematics 155(12) (2007) 1562–1595

    Google Scholar 

  27. Darwiche, A.: Model-Based Diagnosis using Structured System Descriptions. Journal of Artificial Intelligence Research 8 (1998) 165–222

    Google Scholar 

  28. Siddiqi, S., Huang, J.: Sequential Diagnosis by Abstraction. Journal of Artificial Intelligence Research 41 (2011) 329–365

    Google Scholar 

  29. Darwiche, A.: A Differential Approach to Inference in Bayesian Networks. Journal of the ACM 50(3) (May 2003) 280–305

    Google Scholar 

  30. Pill, I., Quaritsch, T.: Optimizations for the Boolean Approach to Computing Minimal Hitting Sets. In: ECAI’12. (2012) 648–653

    Google Scholar 

  31. Feldman, A., Provan, G., de Kleer, J., Robert, S., van Gemund, A.: Solving Model-Based Diagnosis Problems with Max-SAT Solvers and Vice Versa. In: DX’10. (2010) 185–192

    Google Scholar 

  32. Metodi, A., Stern, R., Kalech, M., Codish, M.: A Novel SAT-Based Approach to Model Based Diagnosis. Journal of Artificial Intelligence Research 51 (2014) 377–411

    Google Scholar 

  33. Mencia, C., Marques-Silva, J.: Efficient Relaxations of Over-constrained CSPs. In: ICTAI’14. (2014) 725–732

    Google Scholar 

  34. Mencía, C., Previti, A., Marques-Silva, J.: Literal-Based MCS Extraction. In: IJCAI’15. (2015) 1973–1979

    Google Scholar 

  35. Nica, I., Pill, I., Quaritsch, T.,Wotawa, F.: The Route to Success: A Performance Comparison of Diagnosis Algorithms. In: IJCAI’13. (2013) 1039–1045

    Google Scholar 

  36. Shchekotykhin, K., Friedrich, G., Fleiss, P., Rodler, P.: Interactive Ontology Debugging: Two Query Strategies for Efficient Fault Localization. Journal of Web Semantics 12–13 (2012) 88–103

    Google Scholar 

  37. Feldman, A., Provan, G., van Gemund, A.: Approximate Model-Based Diagnosis Using Greedy Stochastic Search. Journal of Artifcial Intelligence Research 38 (2010) 371–413

    Google Scholar 

  38. Li, L., Yunfei, J.: Computing Minimal Hitting Sets with Genetic Algorithm. In: DX’02. (2002) 1–4

    Google Scholar 

  39. Ram, D.J., Sreenivas, T.H., Subramaniam, K.G.: Parallel Simulated Annealing Algorithms. Journal of Parallel and Distributed Computing 37(2) (1996) 207 – 212

    Google Scholar 

  40. Burns, E., Lemons, S., Ruml, W., Zhou, R.: Best-First Heuristic Search for Multicore Machines. Journal of Artificial Intelligence Research 39 (2010) 689–743

    Google Scholar 

  41. Ferguson, C., Korf, R.E.: Distributed Tree Search and its Application to alphabeta Pruning. In: AAAI’88. (1988) 128–132

    Google Scholar 

  42. Brüngger, A., Marzetta, A., Fukuda, K., Nievergelt, J.: The Parallel Search Bench ZRAM and its Applications. Annals of Operations Research 90(0) (1999) 45–63

    Google Scholar 

  43. Kalyanpur, A., Parsia, B., Horridge, M., Sirin, E.: Finding All Justifications of OWL DL Entailments. In: ISWC 2007 + ASWC 2007. (2007) 267–280

    Google Scholar 

  44. Previti, A., Ignatiev, A., Morgado, A., Marques-Silva, J.: Prime Compilation of Non-Clausal Formulae. In: IJCAI’15. (2015) 1980–1987

    Google Scholar 

  45. Powley, C., Korf, R.E.: Single-agent Parallel Window Search. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(5) (1991) 466–477

    Google Scholar 

  46. Anglano, C., Portinale, L.: Parallel Model-based Diagnosis using PVM. In: EuroPVM’96. (1996) 331–334

    Google Scholar 

  47. Wotawa, F.: A Variant of Reiter’s Hitting-set Algorithm. Information Processing Letters 79(1) (2001) 45–51

    Google Scholar 

  48. Phillips, M., Likhachev, M., Koenig, S.: PA*SE: Parallel A* for Slow Expansions. In: ICAPS’14. (2014)

    Google Scholar 

  49. Korf, R.E., Schultze, P.: Large-scale Parallel Breadth-first Search. In: AAAI’05. (2005) 1380–1385

    Google Scholar 

  50. Shchekotykhin, K.M., Friedrich, G., Rodler, P., Fleiss, P.: Sequential Diagnosis of High Cardinality Faults in Knowledge-Bases by Direct Diagnosis Generation. In: ECAI’14. (2014) 813–818

    Google Scholar 

  51. Kurtoglu, T., Feldman, A.: Third International Diagnostic Competition (DXC 11). https://sites.google.com/site/dxcompetition2011 (2011) Accessed: 2016-03-15.

  52. Prud’homme, C., Fages, J.G., Lorca, X.: Choco Documentation. (2015) http://www.choco-solver.org.

  53. Cardoso, N., Abreu, R.: A Distributed Approach to Diagnosis Candidate Generation. In: EPIA’13. (2013) 175–186

    Google Scholar 

  54. Abreu, R., van Gemund, A.J.C.: A Low-Cost Approximate Minimal Hitting Set Algorithm and its Application to Model-Based Diagnosis. In: SARA’09. (2009) 2–9

    Google Scholar 

  55. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1) (2008) 107–113

    Google Scholar 

  56. Zhao, X., Ouyang, D.: Deriving All Minimal Hitting Sets Based on Join Relation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(7) (2015) 1063–1076

    Google Scholar 

  57. Lin, L., Jiang, Y.: The computation of Hitting Sets: Review and New Algorithms. Information Processing Letters 86(4) (2003) 177–184

    Google Scholar 

Download references

Acknowledgements

The authors were supported by the Carinthian Science Fund (KWF) under contract KWF-3520/26767/38701, the Austrian Science Fund (FWF) and the German Research Foundation (DFG) under contract numbers I 2144 N-15 and JA 2095/4-1 (Project “Debugging of Spreadsheet Programs”).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kostyantyn Shchekotykhin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shchekotykhin, K., Jannach, D., Schmitz, T. (2018). Parallel Model-Based Diagnosis. In: Hamadi, Y., Sais, L. (eds) Handbook of Parallel Constraint Reasoning. Springer, Cham. https://doi.org/10.1007/978-3-319-63516-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63516-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63515-6

  • Online ISBN: 978-3-319-63516-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics