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Volumn 2, Issue , 2008, Pages 919-924

Computing observation vectors for max-fault min-cardinality diagnoses

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS; ARTIFICIAL INTELLIGENCE; BENCHMARKING; MODAL ANALYSIS;

EID: 57749180383     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (6)

References (17)
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  • 4
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    • de Kleer, J.1    Williams, B.2
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    • Characterizing diagnoses and systems
    • de Kleer, J.; Mackworth, A.; and Reiter, R. 1992. Characterizing diagnoses and systems. AI 56(2-3): 197-222.
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  • 6
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    • The complexity of logic-based abduction
    • Eiter, T., and Gottlob, G. 1995. The complexity of logic-based abduction. Journal of the ACM 42(1):3-42.
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    • Eiter, T.1    Gottlob, G.2
  • 7
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    • A two-step hierarchical algorithm for model-based diagnosis
    • Feldman, A., and van Gemund, A. 2006. A two-step hierarchical algorithm for model-based diagnosis. In Proc. AAAI'06, 827-833.
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    • Feldman, A.1    van Gemund, A.2
  • 8
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    • Feldman, A.; Provan, G.; and van Gemund, A. 2007. Approximate model-based diagnosis using greedy stochastic search. In Proc. SARA'07, 139-154.
    • Feldman, A.; Provan, G.; and van Gemund, A. 2007. Approximate model-based diagnosis using greedy stochastic search. In Proc. SARA'07, 139-154.
  • 9
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    • Unveiling the ISCAS-85 benchmarks: A case study in reverse engineering
    • Hansen, M.; Yalcin, H.; and Hayes, J. 1999. Unveiling the ISCAS-85 benchmarks: A case study in reverse engineering. IEEE Design & Test 16(3):72-80.
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    • Provan, G., and Wang, J. 2007. Automated benchmark model generators for model-based diagnostic inference. In Proc. IJCAI'07, 513-518.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.