Contextualised Ambient Intelligence Through Case-Based Reasoning

  • Anders Kofod-Petersen
  • Agnar Aamodt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


Ambient Intelligence is a research area that has gained a lot of attention in recent years. One of the most important issues for ambient intelligent systems is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently. As the ambient intelligence area has been largely technology driven, the abilities of systems to understand their surroundings have largely been ignored. This work demonstrates the first steps towards an ambient intelligent system, which is able to appreciate the environment and reason about occurring situations. This situation awareness is achieved through knowledge intensive case-based reasoning.


Situation Awareness Smart Home Patient Chart Electronic Patient Record Pervasive Computing 
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.


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  1. 1.
    Weiser, M.: The computer for the 21st century. Scientific American, 94–104 (1991)Google Scholar
  2. 2.
    Satyanarayanan, M.: Pervasive computing: Vision and challenges. IEEE Personal Communications 8, 10–17 (2001)CrossRefGoogle Scholar
  3. 3.
    Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., Burgelman, J.C.: Scenarios for Ambient Intelligence in 2010. Technical report, IST Advisory Group (2001)Google Scholar
  4. 4.
    Schmidt, A., Beigl, M., Gellersen, H.W.: There is more to Context than Location. Computers & Graphics Journal 23, 893–902 (1999)CrossRefGoogle Scholar
  5. 5.
    Liu, H., Maes, P.: What would they think? In: Proceedings of the 2004 International Conference on Intelligent User Interfaces, pp. 38–45. ACM Press, New York (2004)CrossRefGoogle Scholar
  6. 6.
    Brézillon, P.: Task-realization models in contextual graphs. In: Dey, A.K., Kokinov, B., Leake, D.B., Turner, R. (eds.) CONTEXT 2005. LNCS, vol. 3554, pp. 55–68. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Öztürk, P., Aamodt, A.: A context model for knowledge-intensive case-based reasoning. International Journal of Human Computer Studies 48, 331–355 (1998)CrossRefGoogle Scholar
  8. 8.
    Zimmermann, A.: Context-awareness in user modelling: Requirements analysis for a case-based reasoning application. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 718–732. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Ma, T., Kim, Y.D., Ma, Q., Tang, M., Zhou, W.: Context-aware implementation based on cbr for smart home. In: Wireless And Mobile Computing, Networking And Communications, 2005 (WiMob 2005), pp. 112–115. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  10. 10.
    Kwon, O.B., Sadeh, N.: Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping. Decision Support Systems 37, 199–213 (2004)Google Scholar
  11. 11.
    Vygotsky, L.S.: Mind in Society. Harvard University Press, Cambridge (1978)Google Scholar
  12. 12.
    Plaza, E., Arcos, J.L., Martín, F.J.: Cooperative case-based reasoning. In: Weiss, G. (ed.) ECAI 1996 Workshops. LNCS, vol. 1221, pp. 180–201. Springer, Heidelberg (1997)Google Scholar
  13. 13.
    Díaz-Agudo, B., González-Calero, P.A.: An architecture for knowledge intensive cbr systems. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS, vol. 1898, pp. 37–48. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Myrhaug, H.I., Whitehead, N., Göker, A., Fægri, T.E., Lech, T.C.: AmbieSense – A System and Reference Architecture for Personalised Context-Sensitive Information Services for Mobile Users. In: Markopoulos, P., Eggen, B., Aarts, E., Crowley, J.L. (eds.) EUSAI 2004. LNCS, vol. 3295, pp. 327–338. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Kofod-Petersen, A., Mikalsen, M.: Context: Representation and Reasoning – Representing and Reasoning about Context in a Mobile Environment. Revue d’Intelligence Artificielle 19, 479–498 (2005)CrossRefGoogle Scholar
  16. 16.
    Endsley, M.R., Bolté, B., Jones, D.G.: Designing for Situation Awareness: An Approach to User-Centered Design. Taylor & Francis, Abington (2003)Google Scholar
  17. 17.
    Aamodt, A.: A knowledge-intensive, integrated approach to problem solving and sustained learning. Ph.D thesis, University of Trondheim, Norwegian Institute of Technology, Department of Computer Science, University Microfilms PUB 92-08460 (1991)Google Scholar
  18. 18.
    Fensel, D., Motta, E., Benjamins, V.R., Crubezy, M., Decker, S., Gaspari, M., Groenboom, R., Grosso, W., van Harmelen, F., Musen, M., Plaza, E., Schreiber, G., Studer, R., Wielinga, B.: The unified problem-solving method development language upml. Knowledge and Information Systems 5 (2003)Google Scholar
  19. 19.
    Kofod-Petersen, A., Mikalsen, M.: An Architecture Supporting implementation of Context-Aware Services. In: Floréen, P., Lindén, G., Niklander, T., Raatikainen, K. (eds.) Workshop on Context Awareness for Proactive Systems (CAPS 2005), Helsinki, Finland, pp. 31–42. HIIT Publications (2005)Google Scholar
  20. 20.
    Lech, T.C., Wienhofen, L.W.M.: AmbieAgents: A Scalable Infrastructure for Mobile and Context-Aware Information Services. In: AAMAS 2005: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pp. 625–631. ACM Press, New York (2005)CrossRefGoogle Scholar
  21. 21.
    Gundersen, O.E., Kofod-Petersen, A.: Multiagent Based Problem-solving in a Mobile Environment. In: Coward, E. (ed.) Norsk Informatikkonferance 2005, NIK 2005, Institutt for Informatikk, Universitetet i Bergen, pp. 7–18 (2005)Google Scholar
  22. 22.
    Aamodt, A.: Knowledge-intensive case-based reasoning in creek. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 1–15. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Aamodt, A.: Modeling the knowledge contents of CBR systems. In: Proceedings of the Workshop Program at the Fourth International Conference on Case-Based Reasoning, Vancouver. Naval Research Laboratory Technical Note AIC-01-003, pp. 32–37 (2001)Google Scholar
  24. 24.
    Kofod-Petersen, A., Cassens, J.: Activity Theory and Context-Awareness. In: Schulza, S., Leake, D.B., Roth-Berghofer, T.R. (eds.) Proceedings of the IJCAI-05 Workshop on Modeling and Retrieval of Context (MRC 2005). CEUR Workshop Proceedings, vol. 146, pp. 1–12 (2005) Google Scholar
  25. 25.
    Cassens, J., Kofod-Petersen, A.: Using activity theory to model context awareness: a qualitative case study. In: Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference, Florida, USA. AAAI Press, Menlo Park (2006)Google Scholar
  26. 26.
    Göker, A., Myrhaug, H.I.: User context and personalisation. In: Workshop proceedings for the 6th European Conference on Case Based Reasoning (2002)Google Scholar
  27. 27.
    Gu, M., Aamodt, A.: Dialog learning in conversational cbr. In: Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference, Florida, USA. AAAI Press, Menlo Park (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anders Kofod-Petersen
    • 1
  • Agnar Aamodt
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway

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