International Conference on Logic Programming and Nonmonotonic Reasoning

LPNMR 2015: Logic Programming and Nonmonotonic Reasoning pp 228-241 | Cite as

Knowledge Acquisition via Non-monotonic Reasoning in Distributed Heterogeneous Environments

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9345)


The role of data and knowledge exchange is becoming increasingly important. The approach of DACMAS [1] proposes a quite general modeling of Multi-Agent Systems (MAS), including data representation in a MAS via DRL-Lite Ontologies. Yet, data/knowledge acquisition from heterogeneous sources which are not agents and which are external to the MAS is not provided. In the Knowledge Representation and Reasoning field, this topic is coped with by mMCSs (Managed Multi-Context Systems). In this paper, we propose an integration of the two approaches into DACMACSs. The aim is to obtain an enhanced integrated flexible framework where non-monotonicity is present: in the modalities for defining knowledge acquisition; in the conditions for triggering the acquisition and for knowledge exploitation.


  1. 1.
    Montali, M., Calvanese, D., De Giacomo, G.: Verification of data-aware commitment-based multiagent system. In: Proceedings of AAMAS (2014)Google Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)Google Scholar
  3. 3.
    Omicini, A., Ricci, A., Viroli, M.: Artifacts in the a&a meta-model for multi-agent systems. Auton. Agent. Multi-Agent Syst. 17(3), 432–456 (2008)CrossRefGoogle Scholar
  4. 4.
    Brewka, G., Eiter, T.: Equilibria in heterogeneous nonmonotonic multi-context systems. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence, pp. 385–390. AAAI Press (2007)Google Scholar
  5. 5.
    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. LNCS, vol. 6565, pp. 233–258. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  6. 6.
    Brewka, G., Ellmauthaler, S., Pührer, J.: Multi-context systems for reactive reasoning in dynamic environments. In: ECAI 2014, Proceedings of the 21st European Conference on Artificial Intelligence. IJCAI/AAAI (2014)Google Scholar
  7. 7.
    Brewka, G., Eiter, T., Fink, M., Weinzierl, A.: Managed multi-context systems. In: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI/AAAI, pp. 786–791 (2011)Google Scholar
  8. 8.
    Apt, K.R., Bol, R.: Logic programming and negation: a survey. J. Log. Program. 19–20, 9–71 (1994)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Singh, M.P.: Commitments in multiagent systems: some history, some confusions, some controversies, some prospects. In: Paglieri, F., Tummolini, L., Falcone, R., Miceli, M. (eds.) The Goals of Cognition. Essays in Honor of Cristiano Castelfranchi, pp. 601–626. College Publications, London (2012)Google Scholar
  10. 10.
    Costantini, S.: Self-checking logical agents. In: Proceedings of the Eighth Latin American Workshop on Logic, Languages, Algorithms and New Methods of Reasoning LA-NMR 2012. CEUR Workshop Proceedings of the vol. 911, pp. 3–30 (2012). Invited Paper, Extended Abstract in Proceedings of the AAMAS 2013Google Scholar
  11. 11.
    Costantini, S., De Gasperis, G.: Runtime self-checking via temporal (meta-)axioms for assurance of logical agent systems. In: Proceedings of the LAMAS 2014, 7th Workshop on Logical Aspects of Multi-Agent Systems, held at AAMAS 2014, pp. 241–255 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria e Scienze dell’Informazione e Matematica (DISIM)Universitá degli Studi dell’AquilaL’AquilaItaly

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