A Rule-Based Contextual Reasoning Platform for Ambient Intelligence Environments

  • Assaad Moawad
  • Antonis Bikakis
  • Patrice Caire
  • Grégory Nain
  • Yves Le Traon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)

Abstract

The special characteristics and requirements of intelligent environments impose several challenges to the reasoning processes of Ambient Intelligence systems. Such systems must enable heterogeneous entities operating in open and dynamic environments to collectively reason with imperfect context information. Previously we introduced Contextual Defeasible Logic (CDL) as a contextual reasoning model that addresses most of these challenges using the concepts of context, mappings and contextual preferences. In this paper, we present a platform integrating CDL with Kevoree, a component-based software framework for Dynamically Adaptive Systems. We explain how the capabilities of Kevoree are exploited to overcome several technical issues, such as communication, information exchange and detection, and explain how the reasoning methods may be further extended. We illustrate our approach with a running example from Ambient Assisted Living.

Keywords

contextual reasoning distributed reasoning Ambient Intelligence system development 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agostini, A., Bettini, C., Riboni, D.: Experience Report: Ontological Reasoning for Context-aware Internet Services. In: Proceedings of PERCOMW 2006. IEEE Computer Society, Washington, DC (2006)Google Scholar
  2. 2.
    Antoniou, G., Bikakis, A., Karamolegou, A., Papachristodoulou, N., Stratakis, M.: A context-aware meeting alert using semantic web and rule technology. International Journal of Metadata Semantics and Ontologies 2(3), 147–156 (2007)CrossRefGoogle Scholar
  3. 3.
    Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: Representation results for defeasible logic. ACM Transactions on Computational Logic 2(2), 255–287 (2001)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Bikakis, A., Antoniou, G.: Defeasible Contextual Reasoning with Arguments in Ambient Intelligence. IEEE Trans. on Knowledge and Data Engineering 22(11), 1492–1506 (2010)CrossRefGoogle Scholar
  5. 5.
    Bikakis, A., Antoniou, G.: Rule-based contextual reasoning in ambient intelligence. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 74–88. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Bikakis, A., Antoniou, G.: Partial preferences and ambiguity resolution in contextual defeasible logic. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 193–198. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Bikakis, A., Antoniou, G., Hassapis, P.: Strategies for contextual reasoning with conflicts in Ambient Intelligence. Knowledge and Information Systems 27(1), 45–84 (2011)CrossRefGoogle Scholar
  8. 8.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 277–298 (2009)Google Scholar
  9. 9.
    Efthymiou, V., Caire, P., Bikakis, A.: Modeling and evaluating cooperation in multi-context systems using conviviality. In: Proceedings of BNAIC 2012 The 24th Benelux Conference on Artificial Intelligence, pp. 83–90 (2012)Google Scholar
  10. 10.
    Fouquet, F., Barais, O., Plouzeau, N., Jézéquel, J.M., Morin, B., Fleurey, F.: A Dynamic Component Model for Cyber Physical Systems. In: 15th International ACM SIGSOFT Symposium on Component Based Software Engineering, Bertinoro, Italie (July 2012), http://hal.inria.fr/hal-00713769
  11. 11.
    Fouquet, F., Nain, G., Morin, B., Daubert, E., Barais, O., Plouzeau, N., Jézéquel, J.-M.: An Eclipse Modelling Framework Alternative to Meet the Models@Runtime Requirements. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 87–101. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Ghidini, C., Giunchiglia, F.: Local Models Semantics, or contextual reasoning=locality+compatibility. Artificial Intelligence 127(2), 221–259 (2001)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Giunchiglia, F., Serafini, L.: Multilanguage hierarchical logics, or: how we can do without modal logics. Artificial Intelligence 65(1) (1994)Google Scholar
  14. 14.
    Gu, T., Pung, H.K., Zhang, D.Q.: A Middleware for Building Context-Aware Mobile Services. In: Proceedings of the IEEE Vehicular Technology Conference (VTC 2004), Milan, Italy (May 2004)Google Scholar
  15. 15.
    Henricksen, K., Indulska, J.: Modelling and Using Imperfect Context Information. In: Proceedings of PERCOMW 2004, pp. 33–37. IEEE Computer Society, Washington, DC (2004)Google Scholar
  16. 16.
    Khushraj, D., Lassila, O., Finin, T.: sTuples: Semantic Tuple Spaces. In: First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous 2004), pp. 267–277 (August 2004)Google Scholar
  17. 17.
    Korpipaa, P., Mantyjarvi, J., Kela, J., Keranen, H., Malm, E.J.: Managing Context Information in Mobile Devices. IEEE Pervasive Computing 02(3), 42–51 (2003)CrossRefGoogle Scholar
  18. 18.
    Krummenacher, R., Kopecký, J., Strang, T.: Sharing Context Information in Semantic Spaces. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM Workshops 2005. LNCS, vol. 3762, pp. 229–232. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Luckham, D.C.: The power of events - an introduction to complex event processing in distributed enterprise systems. ACM (2005)Google Scholar
  20. 20.
    Mileo, A., Merico, D., Pinardi, S., Bisiani, R.: A logical approach to home healthcare with intelligent sensor-network support. Comput. J. 53(8), 1257–1276 (2010)CrossRefGoogle Scholar
  21. 21.
    Moawad, A., Efthymiou, V., Caire, P., Nain, G., Le Traon, Y.: Introducing conviviality as a new paradigm for interactions among IT objects. In: Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments, vol. 907, pp. 3–8. CEUR-WS.org (2012)Google Scholar
  22. 22.
    Morin, B., Barais, O., Nain, G., Jezequel, J.M.: Taming dynamically adaptive systems using models and aspects. In: Proceedings of the 31st International Conference on Software Engineering, ICSE 2009, Washington, DC, USA, pp. 122–132 (2009), http://dx.doi.org/10.1109/ICSE.2009.5070514
  23. 23.
    Paschke, A., Vincent, P., Springer, F.: Standards for complex event processing and reaction rules. In: Olken, F., Palmirani, M., Sottara, D. (eds.) RuleML - America 2011. LNCS, vol. 7018, pp. 128–139. Springer, Heidelberg (2011)Google Scholar
  24. 24.
    Ranganathan, A., Campbell, R.H.: An infrastructure for context-awareness based on first order logic. Personal Ubiquitous Comput. 7(6), 353–364 (2003)CrossRefGoogle Scholar
  25. 25.
    Toninelli, A., Montanari, R., Kagal, L., Lassila, O.: A Semantic Context-Aware Access Control Framework for Secure Collaborations in Pervasive Computing Environments. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 473–486. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Assaad Moawad
    • 1
  • Antonis Bikakis
    • 2
  • Patrice Caire
    • 1
  • Grégory Nain
    • 1
  • Yves Le Traon
    • 1
  1. 1.SnTUniversity of LuxembourgLuxembourg
  2. 2.Department of Information StudiesUniversity CollegeLondonUK

Personalised recommendations