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Computing all minimal hitting sets by subset recombination

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Abstract

In model-based diagnosis from first principles, the efficient computation of all minimal hitting sets (MHS) as candidates for the conflict component sets of a device is a vital task. However, deriving all MHS is NP-hard. In this paper, the principle of “Divide and Conquer” is used to decompose a large family of conflict sets into many smaller sub-families. To efficiently merge the sub-MHS to give sub-families of conflict sets, the relations between the sub-MHS and sub-families of conflict sets are exploited. Based on this, a new method called Subset-Rec-MHS is proposed. In theory, our method based on sub-MHS recombination generally has lower complexity than that based on whole MHS families, as it avoids a large number of set unions and comparisons (to minimize the family of hitting sets). Compared with the direct merge of whole MHS families, the proposed approach reduces the computation time by a factor of approximately \(\frac {7}{16}\). Experimental results on both synthetic examples and ISCAS-85 benchmark circuit conflict sets show that, in many cases, our approach offers better performance than previous algorithms.

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Notes

  1. In this paper, to discriminate different types of symbols, we use lower-caseletters (such as e), upper-case letters (such as S), and calligraphic letters (such as\(\mathcal {F}\))to denote the basic elements of a set, basic sets including some basic elements, and families of some basic sets, respectively.

  2. Generally, in model-based diagnosis, there is an assumption that the connections between components are workingnormally.

  3. As we use a two-way divide and merge strategy to derive the MHS, we assume that the family of conflict sets is organized as 2X groups, with each group having elements that are different from those in other groups.

  4. The rationale for using such synthetic instances is twofold. First, the cardinality of each MHS for such instances is generally not very large, and so generating such MHS is usually fast. Second, these structures are properly regular and can easily be extended to larger sizes.

  5. The package of conflict sets for ISCAS-85 benchmark circuits can be downloaded from http://www.fe.up.pt/~rma/benchmarks2.zip or extracted from the Lydia software from https://general-diagnostics.com/downloads.php. The package can also be requested by email from the authors.

References

  1. Abreu R, van Gemund A (2009) A low-cost approximate minimal hitting set algorithm and its application to model-based diagnosis. In: Proceedings of the 8th symposium on abstraction, reformulation, and approximation (SARA-09). AAAI Press, pp 2–9

    Google Scholar 

  2. Boros E, Makino K (2009) A fast and simple parallel algorithm for the monotone duality problem. In: Proceedings of the 36th international colloquium on automata, languages and programming (ICALP-09). Springer, Berlin, Heidelberg, pp 183– 194

    Google Scholar 

  3. Brglez F, Fujiwara H (1985) A neutral netlist of 10 combinational benchmark circuits and a target translator in fortran. In: Proceedings of the 1985 IEEE international symposium on circuits and systems (ISCAS-85). IEEE, pp 695–698

    Google Scholar 

  4. Cardoso N, Abreu R (2013) MHS 2: a map-reduce heuristic-driven minimal hitting set search algorithm. In: Proceedings of the 2nd international conference on multicore software engineering, performance, and tools (MUSEPAT-13), pp 25–36

    Google Scholar 

  5. Chan W, Chen C (2016) Consensus control with failure – wait or abandon? IEEE Trans Cybern 46:75–84. doi:10.1109/TCYB.2015.2394471

    Article  Google Scholar 

  6. Crama Y, Hammer PL (2011) Boolean functions - theory, algorithms, and applications, encyclopedia of mathematics and its applications, vol 142. Cambridge University Press

  7. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51 (1):107–113

    Article  Google Scholar 

  8. Eiter T, Makino K, Gottlob G (2008) Computational aspects of monotone dualization: a brief survey. Discret Appl Math 156(11):2035–2049

    Article  MathSciNet  MATH  Google Scholar 

  9. Feldman A, Provan G, van Gemund A (2008) Computing minimal diagnoses by greedy stochastic search. In: Proceedings of the 23rd national conference on artificial intelligence (AAAI-08). AAAI Press, pp 911–918

    Google Scholar 

  10. Fijany A, Vatan F (2004) New approaches for efficient solution of hitting set problem. In: Proceedings of the winter international symposium on information and communication technologies (WISICT-04), pp 1–10

    Google Scholar 

  11. Freitag H, Friedrich G (1992) Focusing on independent diagnosis problems. In: Nebel B, Rich C, Swartout WR (eds). In: Proceedings of the 3rd international conference on principles of knowledge representation and reasoning (KR-92). Morgan Kaufmann, pp 521–531

    Google Scholar 

  12. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W. H. Freeman & Co., New York, NY, USA

    MATH  Google Scholar 

  13. Greiner R, Smith BA, Wilkerson RW (1989) A correction to the algorithm in reiter’s theory of diagnosis. Artif Intell 41(1):79–88

    Article  MathSciNet  MATH  Google Scholar 

  14. Jannach D, Schmitz T, Shchekotykhin K (2014) Parallelized hitting set computation for model-based diagnosis. In: Proceedings of the 25th international workshop on principles of diagnosis (DX-14)

    Google Scholar 

  15. Jannach D, Schmitz T, Shchekotykhin K (2015) Parallelized hitting set computation for model-based diagnosis. In: Proceedings of the 29th AAAI conference on artificial intelligence (AAAI-15), pp 1503–1510

    Google Scholar 

  16. Jannach D, Schmitz T, Shchekotykhin K (2016) Parallel model-based diagnosis on multi-core computers. J Artif Intell Res 55:835–887

    MathSciNet  MATH  Google Scholar 

  17. Junker U (2004) Quickxplain: preferred explanations and relaxations for over-constrained problems. In: Proceedings of the 19th national conference on artifical intelligence (AAAI-04). AAAI Press, pp 167–172

    Google Scholar 

  18. Karp R (1972) Reducibility among combinatorial problems. In: Miller R, Thatcher J (eds) Complexity of computer computations. Plenum Press, New York

    Google Scholar 

  19. Khachiyan L, Boros E, Elbassioni K, Gurvich V (2007) A global parallel algorithm for the hypergraph transversal problem. Inf Process Lett 101(4):148–155

    Article  MathSciNet  MATH  Google Scholar 

  20. de Kleer J (2011) Hitting set algorithms for model-based diagnosis. In: Proceedings of the 22nd international workshop on principles of diagnosis (DX-11), pp 100–105

    Google Scholar 

  21. de Kleer J, Williams BC (1987) Diagnosing multiple faults. Artif Intell 32(1):97–130

    Article  MATH  Google Scholar 

  22. Kwong RH, Yonge-Mallo DL (2015) Fault diagnosis in discrete-event systems with incomplete models: learnability and diagnosability. IEEE Trans Cybern 45:1236–1249. doi:10.1109/TCYB.2014.2347801

    Article  Google Scholar 

  23. Lamperti G, Zanella M (2003) EDEN: an intelligent software environment for diagnosis of discrete-event systems. Appl Intell 18(1):55–77

    Article  MATH  Google Scholar 

  24. Lamperti G, Zanella M (2004) A bridged diagnostic method for the monitoring of polymorphic discrete-event systems. IEEE Trans Syst Man Cybern Part B Cybern 34(5):2222–2244

    Article  Google Scholar 

  25. Liffiton MH, Sakallah KA (2008) Algorithms for computing minimal unsatisfiable subsets of constraints. J Autom Reason 40(1):1–33

    Article  MathSciNet  MATH  Google Scholar 

  26. Lin L, Jiang Y (2002) Computing minimal hitting sets with genetic algorithm. In: Proceedings of the 13th international workshop on principles of diagnosis (DX-02), pp 77–80

    Google Scholar 

  27. Lin L, Jiang Y (2003) The computation of hitting sets: review and new algorithms. Inf Process Lett 86 (4):177–184

    Article  MathSciNet  MATH  Google Scholar 

  28. Luo M, Li Y, Sun F, Liu H (2012) A new algorithm for testing diagnosability of fuzzy discrete event systems. Inf Sci 185(1):100–113

    Article  MathSciNet  MATH  Google Scholar 

  29. Marques-Silva J, Janota M, Belov A (2013) Minimal sets over monotone predicates in boolean formulae. Lect Notes Comput Sci 8044:592–607

    Article  MathSciNet  Google Scholar 

  30. Nyberg M (2011) A generalized minimal hitting-set algorithm to handle diagnosis with behavioral modes. IEEE Trans Syst Man Cybern Part A Syst Humans 41(1):137–148

    Article  Google Scholar 

  31. Pill I, Quaritsch T (2012) Optimizations for the boolean approach to computing minimal hitting sets. In: Proceedings of the 20th european conference on artificial intelligence (ECAI-12). IOS Press, pp 648–653

    Google Scholar 

  32. Previti A, Ignatiev A, Morgado A, Marques-Silva J (2015) Prime compilation of non-clausal formulae. In: Proceedings of the 24th international joint conference on artificial intelligence (IJCAI-15), pp 1980–1987

    Google Scholar 

  33. Reiter R (1987) A theory of diagnosis from first principles. Artif Intell 32(1):57–95

    Article  MathSciNet  MATH  Google Scholar 

  34. Shah I (2011) A hybrid algorithm for finding minimal unsatisfiable subsets in over-constrained csps. Int J Intell Syst 26(11):1023–1048. doi:10.1002/int.20497

    Article  Google Scholar 

  35. Shchekotykhin K, Jannach D, Schmitz T (2015) Mergexplain: fast computation of multiple conflicts for diagnosis. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence (IJCAI-15). AAAI Press, pp 3221–3228

    Google Scholar 

  36. Stern R, Kalech M, Feldman A, Provan G (2012) Exploring the duality in conflict-directed model-based diagnosis. In: Proceedings of the 26th AAAI conference on artificial intelligence (AAAI-12). AAAI Press, pp 828–834

    Google Scholar 

  37. Vinterbo S, Ohrn A (2000) Minimal approximate hitting sets and rule templates. Int J Approx Reason 25 (2):123–143

    Article  MathSciNet  MATH  Google Scholar 

  38. Weber J, Wotawa F (2012) Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot. Appl Intell 36(3):511–528

    Article  Google Scholar 

  39. Wikipedia (2015) Set cover problem. https://en.wikipedia.org/wiki/set_cover_problem

  40. Williams BC, Ragno RJ (2007) Conflict-directed a* and its role in model-based embedded systems. Discret Appl Math 155(12):1562–1595. doi:10.1016/j.dam.2005.10.022

    Article  MathSciNet  MATH  Google Scholar 

  41. Wotawa F (2001) A variant of reiter’s hitting-set algorithm. Inf Process Lett 79(1):45–51

    Article  MathSciNet  MATH  Google Scholar 

  42. Zhang L, Zeng H, Yang F, Ouyang D (2011) Dynamic theorem proving algorithm for consistency-based diagnosis. Expert Syst Appl 38:7511–7516

    Article  Google Scholar 

  43. Zhao X, Ouyang D (2006) A method of combining SE-tree to compute all minimal hitting sets. Prog Nat Sci 16(2):169–174

    Article  MathSciNet  MATH  Google Scholar 

  44. Zhao X, Ouyang D (2007) Improved algorithms for deriving all minimal conflict sets in model-based diagnosis. In: Proceedings of the 3rd international conference on intelligent computing (ICIC-07), lecture notes in computer science, vol. 4681. Springer, pp 157–166

    Google Scholar 

  45. Zhao X, Ouyang D (2008) Model-based diagnosis of discrete event systems with an incomplete system model. In: Proceedings of the 18th European conference on artificial intelligence (ECAI-08). IOS Press, pp 189–193

    Google Scholar 

  46. Zhao X, Ouyang D (2013) A distributed strategy for deriving minimal hitting-sets. In: Proceedings of the 24th international workshop on principles of diagnosis (DX-13), pp 33–38

    Google Scholar 

  47. Zhao X, Ouyang D (2015) Deriving all minimal hitting sets based on join relation. IEEE Trans Syst Man Cybern Syst 45(7):1063–1076

    Article  Google Scholar 

  48. Zhao X, Ouyang D (2016) Deriving all minimal hitting-sets by merging. In: Proceedings of the IEEE international conference on information and automation (ICIA-2016), pp 1132–1137

    Google Scholar 

  49. Zhao X, Ouyang D, Zhang L, Wang X, Mo Y (2012) Reasoning on partially-ordered observations in online diagnosis of dess. AI Commun 25(4):285–294

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the anonymous referees for their constructive comments, which considerably improved the quality of the manuscript.

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Correspondence to Xiangfu Zhao.

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This work was supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY16F020004 and the National Natural Science Foundation of China under Grant Nos. 61003101, 61272208, and 61472369.

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Zhao, X., Ouyang, D. & Zhang, L. Computing all minimal hitting sets by subset recombination. Appl Intell 48, 257–270 (2018). https://doi.org/10.1007/s10489-017-0971-7

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