Justifications and Blocking Sets in a Rule-Based Answer Set Computation

Authors Christopher Béatrix, Claire Lefèvre, Laurent Garcia, Igor Stéphan



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Christopher Béatrix
Claire Lefèvre
Laurent Garcia
Igor Stéphan

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Christopher Béatrix, Claire Lefèvre, Laurent Garcia, and Igor Stéphan. Justifications and Blocking Sets in a Rule-Based Answer Set Computation. In Technical Communications of the 32nd International Conference on Logic Programming (ICLP 2016). Open Access Series in Informatics (OASIcs), Volume 52, pp. 6:1-6:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016) https://doi.org/10.4230/OASIcs.ICLP.2016.6

Abstract

Notions of justifications for logic programs under answer set semantics have been recently studied for atom-based approaches or argumentation approaches. The paper addresses the question in a rule-based answer set computation: the search algorithm does not guess on the truth or falsity of an atom but on the application or non application of a non monotonic rule. In this view, justifications are sets of ground rules with particular properties. Properties of these justifications are established; in particular the notion of blocking set (a reason incompatible with an answer set) is defined, that permits to explain computation failures. Backjumping, learning, debugging and explanations are possible applications.

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Keywords
  • Answer Set Programming
  • Justification
  • Rule-based Computation

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References

  1. C. V. Damásio, A. Analyti, and G. Antoniou. Justifications for logic programming. In LPNMR 2013,, pages 530-542, 2013. Google Scholar
  2. C. V. Damásio, J. Moura, and A. Analyti. Unifying justifications and debugging for answer-set programs. In ICLP 2015, 2015. Google Scholar
  3. M. Dao-Tran, T. Eiter, M. Fink, G. Weidinger, and A. Weinzierl. OMiGA: An open minded grounding on-the-fly answer set solver. In JELIA 2012, pages 480-483, 2012. Google Scholar
  4. T. Eiter, M. Fink, P. Schüller, and A. Weinzierl. Finding explanations of inconsistency in multi-context systems. In KR 2010, 2010. Google Scholar
  5. M. Gebser, B. Kaufmann, A. Neumann, and T. Schaub. Conflict-driven answer set solving. In IJCAI 2007, pages 386-392, 2007. Google Scholar
  6. M. Gebser, J. Pührer, T. Schaub, and H. Tompits. A meta-programming technique for debugging answer-set programs. In AAAI 2008, pages 448-453, 2008. Google Scholar
  7. M. Gelfond and V. Lifschitz. The stable model semantics for logic programming. In Logic Programming, Proceedings of the Fifth International Conference and Symposium, pages 1070-1080, 1988. Google Scholar
  8. K. Konczak, T. Linke, and T. Schaub. Graphs and colorings for answer set programming. Theory and Practice of Logic Programming, 6:61-106, 1 2006. URL: http://dx.doi.org/10.1017/S1471068405002528.
  9. C. Lefèvre, C. Béatrix, I. Stéphan, and L. Garcia. Asperix, a first order forward chaining approach for answer set computing. CoRR, abs/1503.07717:(to appear in TPLP), 2015. URL: http://arxiv.org/abs/1503.07717.
  10. C. Lefèvre and P. Nicolas. A first order forward chaining approach for answer set computing. In LPNMR 2009, pages 196-208, 2009. Google Scholar
  11. C. Lefèvre and P. Nicolas. The first version of a new ASP solver : ASPeRiX. In LPNMR 2009, pages 522-527, 2009. Google Scholar
  12. N. Leone, G. Pfeifer, W. Faber, T. Eiter, G. Gottlob, S. Perri, and F. Scarcello. The DLV system for knowledge representation and reasoning. ACM Transactions on Computational Logic, 7(3):499-562, 2006. URL: http://dx.doi.org/10.1145/1149114.1149117.
  13. L. Liu, E. Pontelli, T. C. Son, and M. Truszczynski. Logic programs with abstract constraint atoms: The role of computations. Artificial Intelligence, 174(3-4):295-315, 2010. URL: http://dx.doi.org/10.1016/j.artint.2009.11.016.
  14. J. Oetsch, J. Pührer, and H. Tompits. Stepping through an answer-set program. In LPNMR 2011, pages 134-147, 2011. Google Scholar
  15. A. Dal Palù, A. Dovier, E. Pontelli, and G. Rossi. Answer set programming with constraints using lazy grounding. In ICLP 2009, 2009. Google Scholar
  16. E. Pontelli, T. C. Son, and O. El-Khatib. Justifications for logic programs under answer set semantics. Theory and Practice of Logic Programming, 9(1):1-56, 2009. URL: http://dx.doi.org/10.1017/S1471068408003633.
  17. C. Schulz and F. Toni. Justifying answer sets using argumentation. Theory and Practice of Logic Programming, 16(1):59-110, 2016. URL: http://dx.doi.org/10.1017/S1471068414000702.
  18. P. Simons, I. Niemelä, and T. Soininen. Extending and implementing the stable model semantics. Artificial Intelligence, 138(1-2):181-234, 2002. URL: http://dx.doi.org/10.1016/S0004-3702(02)00187-X.
  19. A. Weinzierl. Learning non-ground rules for answer-set solving. In 2nd Workshop on Grounding and Transformations for Theories with Variables, GTTV 2013, 2013. Google Scholar
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