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Using rules of thumb for repairing inconsistent answer set programs

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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Answer set programming is a form of declarative programming that can be used to elegantly model various systems. When the available knowledge about these systems is imperfect, however, the resulting programs can be inconsistent. In such cases, it is of interest to find plausible repairs, i.e.~plausible modifications to the original program that ensure the existence of at least one answer set. Although several approaches to this end have already been proposed, most of them merely find a repair which is in some sense minimal. In many applications, however, expert knowledge is available which could allow us to identify better repairs. In this paper, we analyze the potential of using expert knowledge in this way, by focusing on a specific case study: gene regulatory networks. We show how we can identify the repairs that best agree with insights about such networks that have been reported in the literature, and experimentally compare this strategy against the baseline strategy of identifying minimal repairs.
Keywords
Inconsistency, GENETIC REGULATORY NETWORKS, Rules of thumb, Answer Set Program, BOOLEAN NETWORKS, ATTRACTORS

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Chicago
Merhej, Elie, Steven Schockaert, and Martine De Cock. 2015. “Using Rules of Thumb for Repairing Inconsistent Answer Set Programs.” In Lecture Notes in Artificial Intelligence, ed. C Beierle and A Dekhtyar, 9310:368–381. Berlin, Germany: Springer.
APA
Merhej, E., Schockaert, S., & De Cock, M. (2015). Using rules of thumb for repairing inconsistent answer set programs. In C. Beierle & A. Dekhtyar (Eds.), Lecture Notes in Artificial Intelligence (Vol. 9310, pp. 368–381). Presented at the 9th International conference on Scalable Uncertainty Management (SUM), Berlin, Germany: Springer.
Vancouver
1.
Merhej E, Schockaert S, De Cock M. Using rules of thumb for repairing inconsistent answer set programs. In: Beierle C, Dekhtyar A, editors. Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer; 2015. p. 368–81.
MLA
Merhej, Elie, Steven Schockaert, and Martine De Cock. “Using Rules of Thumb for Repairing Inconsistent Answer Set Programs.” Lecture Notes in Artificial Intelligence. Ed. C Beierle & A Dekhtyar. Vol. 9310. Berlin, Germany: Springer, 2015. 368–381. Print.
@inproceedings{6854760,
  abstract     = {Answer set programming is a form of declarative programming that can be used to elegantly model various systems. When the available knowledge about these systems is imperfect, however, the resulting programs can be inconsistent. In such cases, it is of interest to find plausible repairs, i.e.{\texttildelow}plausible modifications to the original program that ensure the existence of at least one answer set. Although several approaches to this end have already been proposed, most of them merely find a repair which is in some sense minimal. In many applications, however, expert knowledge is available which could allow us to identify better repairs. In this paper, we analyze the potential of using expert knowledge in this way, by focusing on a specific case study: gene regulatory networks. We show how we can identify the repairs that best agree with insights about such networks that have been reported in the literature, and experimentally compare this strategy against the baseline strategy of identifying minimal repairs.},
  author       = {Merhej, Elie and Schockaert, Steven and De Cock, Martine},
  booktitle    = {Lecture Notes in Artificial Intelligence},
  editor       = {Beierle, C and Dekhtyar, A},
  isbn         = {9783319235400},
  issn         = {0302-9743},
  keyword      = {Inconsistency,GENETIC REGULATORY NETWORKS,Rules of thumb,Answer Set Program,BOOLEAN NETWORKS,ATTRACTORS},
  language     = {eng},
  location     = {Qu{\'e}bec, QB, Canada},
  pages        = {368--381},
  publisher    = {Springer},
  title        = {Using rules of thumb for repairing inconsistent answer set programs},
  url          = {http://dx.doi.org/10.1007/978-3-319-23540-0\_25},
  volume       = {9310},
  year         = {2015},
}

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