Bridge Rules for Reasoning in Component-Based Heterogeneous Environments

  • Stefania Costantini
  • Giovanni De Gasperis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9718)


Multi-Context Systems (MCS) model in Computational Logic distributed systems composed of heterogeneous sources, or “contexts”, interacting via special rules called “bridge rules”. In this paper we consider how to enhance flexibility and generality of such systems; in particular, we discuss aspects that might be improved to increase practical applicability.


  1. 1.
    Brewka, G., Eiter, T.: Equilibria in heterogeneous nonmonotonic multi-context systems. In: Proceedings of 22nd AAAI Conference on Artificial Intelligence, pp. 385–390. AAAI Press (2007)Google Scholar
  2. 2.
    Brewka, G., Eiter, T., Fink, M.: Nonmonotonic multi-context systems: a flexible approach for integrating heterogeneous knowledge sources. In: Balduccini, M., Son, T.C. (eds.) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning: Essays Dedicated to Michael Gelfond on the Occasion of His 65th Birthday. LNCS, vol. 6565, pp. 233–258. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Brewka, G., Ellmauthaler, S., Pührer, J.: Multi-context systems for reactive reasoning in dynamic environments. In: Schaub, T. (ed.) Proceedings of 21st European Conference on Artificial Intelligence, ECAI 2014. IJCAI/AAAI (2014)Google Scholar
  4. 4.
    Lloyd, J.W.: Foundations of Logic Programming. Springer, Heidelberg (1987)CrossRefzbMATHGoogle Scholar
  5. 5.
    Apt, K.R., Bol, R.N.: Logic programming and negation: a survey. J. Log. Program. 19–20, 9–71 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Brewka, G., Eiter, T., Fink, M., Weinzierl, A.: Managed multi-context systems. In: Walsh, T. (ed.) Proceedings of 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, pp. 786–791. IJCAI/AAAI (2011)Google Scholar
  7. 7.
    Costantini, S.: Knowledge acquisition via non-monotonic reasoning in distributed heterogeneous environments. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS, vol. 9345, pp. 228–241. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Costantini, S., De Gasperis, G.: Exchanging data and ontological definitions in multi-agent-contexts systems. In: Paschke, A., Fodor, P., Giurca, A., Kliegr, T. (eds.) Proceedings of RuleMLChallenge Track, CEUR Workshop Proceedings. (2015)Google Scholar
  9. 9.
    Giunchiglia, F., Serafini, L.: Multilanguage hierarchical logics or: how we can do without modal logics. Artif. Intell. 65(1), 29–70 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Fink, M., Ghionna, L., Weinzierl, A.: Relational information exchange and aggregation in multi-context systems. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 120–133. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Dao-Tran, M., Eiter, T., Fink, M., Krennwallner, T.: Distributed evaluation of nonmonotonic multi-context systems. J. Artif. Int. Res. (JAIR) 52, 543–600 (2015)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Eiter, T., Šimkus, M.: Linking open-world knowledge bases using nonmonotonic rules. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS, vol. 9345, pp. 294–308. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  13. 13.
    Aielli, F., Ancona, D., Caianiello, P., Costantini, S., De Gasperis, G., Di Marco, A., Ferrando, A., Mascardi, V.: FRIENDLY & KIND with your health: human-friendly knowledge-INtensive dynamic systems for the e-Health domain. In: Hallenborg, K., Giroux, S. (eds.) International Workshop on Agents and Multi-agent Systems for AAL and e-HEALTH (A-HEALTH) at PAAMS 2016, Proceedings of Communications in Computer and Information Science. Springer (2016)Google Scholar
  14. 14.
    Gelfond, M.: Answer sets. In: Handbook of Knowledge Representation. Elsevier, Amsterdam (2007)Google Scholar
  15. 15.
    Barilaro, R., Fink, M., Ricca, F., Terracina, G.: Towards query answering in relational multi-context systems. In: Cabalar, P., Son, T.C. (eds.) LPNMR 2013. LNCS, vol. 8148, pp. 168–173. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    Alferes, J.J., Brogi, A., Leite, J., Moniz Pereira, L.: Evolving logic programs. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 50–61. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Bienvenu, M., Lang, J., Wilson, N.: From preference logics to preference languages, and back. In: Proceedings of 12th International Conference on the Principles of Knowledge Representation and Reasoning (KR 2010), pp. 414–424 (2010)Google Scholar
  18. 18.
    Brewka, G., Niemelä, I., Truszczyński, M.: Preferences and nonmonotonic reasoning. AI Mag. 29(4), 69 (2008)Google Scholar
  19. 19.
    Costantini, S., Formisano, A.: Modeling preferences and conditional preferences on resource consumption and production in ASP. J. Algorithms Cogn. Inform. Log. 64(1), 3–15 (2009)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria e Scienze dell’Informazione e MatematicaUniversità degli Studi dell’AquilaL’AquilaItaly

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