Preference-Based Diagnosis Selection in Multi-Context Systems

  • Thomas Eiter
  • Michael Fink
  • Antonius Weinzierl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9060)

Abstract

Nonmonotonic Multi-Context Systems (MCS) provide a rigorous framework and flexible approach to represent and reason over interlinked, heterogeneous knowledge sources. Not least due to nonmonotonicity, however, an MCS may be inconsistent and resolving inconsistency is a major issue. Notions of diagnosis and inconsistency explanations have been developed for this purpose, considering the information exchange as the primary culprit. To discriminate between different possible solutions, we consider preference-based diagnosis selection. We develop a general meta-reasoning technique, i.e., an MCS transformation capable of full introspection on possible diagnoses, and we present a natural encoding of preferred diagnosis selection on top. Moreover, for the more involved notions of diagnosis utilized, we establish that the complexity does not increase. However, this does not carry over to selecting most preferred diagnoses as the encoding is not polynomial.

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References

  1. 1.
    Bikakis, A., Antoniou, G.: Distributed defeasible contextual reasoning in ambient computing. In: Aarts, E., Crowley, J.L., de Ruyter, B., Gerhäuser, H., Pflaum, A., Schmidt, J., Wichert, R. (eds.) AmI 2008. LNCS, vol. 5355, pp. 308–325. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Bikakis, A., Antoniou, G.: Defeasible contextual reasoning with arguments in ambient intelligence. IEEE Trans. Knowl. Data Eng. 22(11), 1492–1506 (2010)CrossRefGoogle Scholar
  3. 3.
    Bikakis, A., Antoniou, G., Hassapis, P.: Strategies for contextual reasoning with conflicts in ambient intelligence. Knowl. Inf. Syst. 27(1), 45–84 (2011)CrossRefGoogle Scholar
  4. 4.
    Brewka, G., Eiter, T.: Equilibria in Heterogeneous Nonmonotonic Multi-Context Systems. In: Proc. 22nd Conf. Artificial Intelligence (AAAI 2007), pp. 385–390. AAAI Press (2007)Google Scholar
  5. 5.
    Brewka, G., Eiter, T., Fink, M., Weinzierl, A.: Managed multi-context systems. In: Walsh, T. (ed.) Proc. 22nd International Joint Conf. Artificial Intelligence (IJCAI 2011), pp. 786–791. AAAI Press/IJCAI (2011)Google Scholar
  6. 6.
    Brewka, G., Roelofsen, F., Serafini, L.: Contextual Default Reasoning. In: Veloso, M. (ed.) Proc. 20th International Joint Conf. Artificial Intelligence (IJCAI 2007), pp. 268–273. AAAI Press/IJCAI (2007)Google Scholar
  7. 7.
    Eiter, T., Fink, M., Schüller, P., Weinzierl, A.: Finding explanations of inconsistency in multi-context systems. Artif. Intell. 216, 233–274 (2014)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Eiter, T., Fink, M., Weinzierl, A.: Preference-based inconsistency assessment in multi-context systems. In: Janhunen, T., Niemelä, I. (eds.) JELIA 2010. LNCS, vol. 6341, pp. 143–155. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Faber, W., Leone, N., Pfeifer, G.: Recursive aggregates in disjunctive logic programs: Semantics and complexity. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 200–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Giunchiglia, F.: Contextual reasoning. Epistemologia XVI, 345–364 (1993)Google Scholar
  11. 11.
    McCarthy, J.: Generality in artificial intelligence. Commun. ACM 30(12), 1029–1035 (1987)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Przymusinski, T.: Stable semantics for disjunctive programs. New Generation Computing 9(3), 401–424 (1991)CrossRefMATHGoogle Scholar
  13. 13.
    Tasharrofi, S., Ternovska, E.: Generalized multi-context systems. In: Baral, C., Giacomo, G.D., Eiter, T. (eds.) Proc. 14th International Conf. Principles of Knowledge Representation and Reasoning (KR 2014), pp. 368–377 (2014)Google Scholar
  14. 14.
    Weinzierl, A.: Inconsistency Management under Preferences for Multi-Context Systems and Extensions. PhD thesis, TU Vienna, A-1040 Vienna, Karlsplatz 13, Austria (October 2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thomas Eiter
    • 1
  • Michael Fink
    • 1
  • Antonius Weinzierl
    • 1
  1. 1.Knowledge-based Systems Group, Institute of Information SystemsVienna University of TechnologyAustria

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