Alternative Strategies for Conflict Resolution in Multi-Context Systems

  • Antonis Bikakis
  • Grigoris Antoniou
  • Panayiotis Hassapis
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


Multi-Context Systems are logical formalizations of distributed context theories connected through mapping rules, which enable information flow between different contexts. Reasoning in Multi-Context Systems introduces many challenges that arise from the heterogeneity of contexts with regard to the language and inference system that they use, and from the potential conflicts that may arise from the interaction of context theories through the mappings. The current paper proposes four alternative strategies for using context and preference information to resolve conflicts in a Multi-Context Framework, in which contexts are modeled as rule theories, mappings as defeasible rules, and global inconsistency is handled using methods of distributed defeasible reasoning.


Conflict Resolution Preference Order Local Rule Mapping Rule Query Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Antoniou G., Billington D., Governatori G., Maher M.J.: Representation results for defeasible logic. ACM Transactions on Computational Logic 2(2):255–287, 2001.MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Antoniou G., Billington D., Governatori G., Maher M.J.: Embedding defeasible logic into logic programming. Theory and Practice of Logic Programming 6(6):703–735, 2006.MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Bikakis A., Antoniou G.: Distributed Defeasible Reasoning in Multi-Context Systems. In NMR′08, pp. 200–206, (2008)Google Scholar
  4. 4.
    Bikakis A., Antoniou G.: Distributed Defeasible Contextual Reasoning in Ambient Computing. In AmI′08 European Conference on Ambient Intelligence, pp. 258–375, (2008)Google Scholar
  5. 5.
    Benerecetti M., Bouquet P., Ghidini C.: Contextual reasoning distilled. JETAI 12(3): 279–305, 2000.MATHGoogle Scholar
  6. 6.
    Brewka G., Roelofsen F., Serafini L.: Contextual Default Reasoning. In: IJCAI, pp. 268–273 (2007)Google Scholar
  7. 7.
    Buvac, S, Mason I.A.: Propositional Logic of Context. In AAAI, pp. 412–419, (1993).Google Scholar
  8. 8.
    Ghidini C., Giunchiglia F.: Local Models Semantics, or contextual reasoning = locality + compatibility. Artificial Intelligence, 127(2):221–259, 2001.MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Giunchiglia F., Serafini L.: Multilanguage hierarchical logics, or: how we can do without modal logics. Artificial Intelligence, 65(1), 1994.Google Scholar
  10. 10.
    McCarthy J.: Generality in Artificial Intelligence. Communications of the ACM, 30(12):1030–1035, 1987.MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    McCarthy J., Buvac S.: Formalizing Context (Expanded Notes). Aliseda A., van Glabbeek R., Westerstahl D. (eds.) Computing Natural Language, pp. 13–50. CSLI Publications, Stanford (1998)Google Scholar
  12. 12.
    Roelofsen F, Serafini L.: Minimal and Absent Information in Contexts. In IJCAI, pp. 558–563, (2005).Google Scholar
  13. 13.
    Serafini L., Bouquet P.: Comparing formal theories of context in AI. Artificial Intelligence, 155(1–2):41–67, 2004.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Antonis Bikakis
    • 1
  • Grigoris Antoniou
    • 1
  • Panayiotis Hassapis
    • 2
  1. 1.Institute of Computer ScienceFO.R.T.H.Greece
  2. 2.Department of Computer ScienceAthens University of Ecomomics and BusinessAthensGreece

Personalised recommendations