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Context Awareness in Ambient Systems by an Adaptive Multi-Agent Approach

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7683)

Abstract

In the field of ambient systems, the dynamic management of user context is needed to allow devices to be proactive in order to adapt to environmental changes and to assist the user in his activities. This proactive approach requires to take into account the dynamics and distribution of devices in the user’s environment, and to have learning capabilities in order to adopt a satisfactory behaviour. This paper presents Amadeus, an Adaptive Multi-Agent System (AMAS), whose objective is to learn, for each device of the ambient system, the contexts for which it can anticipate the user’s needs by performing an action on his behalf. This paper focuses on the Amadeus architecture and on its learning capabilities. It proposes some promising results obtained through various scenarios, including a comparison with the Multilayer Perceptron (MLP) algorithm.

Keywords

  • Context
  • evolution
  • adaptation
  • learning
  • ambient intelligence
  • self-organization

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References

  1. Cook, D.J., Youngblood, M., Heierman III, E.O., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: Mavhome: An agent-based smart home. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), pp. 521–524. IEEE (2003)

    Google Scholar 

  2. Dey, A., Abowd, G., Salber, D.: A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Human-Computer Interaction 16(2), 97–166 (2001)

    CrossRef  Google Scholar 

  3. Euzenat, J., Pierson, J., Ramparany, F.: Dynamic context management for pervasive applications. The Knowledge Engineering Review 23(01), 21–49 (2008)

    CrossRef  Google Scholar 

  4. Georgé, J.-P., Gleizes, M.-P., Camps, V.: Cooperation. In: Di Marzo Serugendo, G., Gleizes, M.-P., Karageorgos, A. (eds.) Self-organising Software. Natural Computing Series, pp. 193–226. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)

    CrossRef  Google Scholar 

  6. Lemouzy, S., Camps, V., Glize, P.: Real Time Learning of Behaviour Features for Personalised Interest Assessment. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds.) Advances in PAAMS. AISC, vol. 70, pp. 5–14. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  7. Lemouzy, S.: Systémes interactifs auto-adaptatifs par systémes multi-agents auto-organisateurs: application á la personnalisation de l’accés á l’information. Thése de doctorat, Université Paul Sabatier, Toulouse, France, juillet (2011)

    Google Scholar 

  8. Noel, V., Arcangeli, J.-P., Gleizes, M.-P.: Between Design and Implementation of Multi-Agent Systems: A Component-Based Two-Step Process (regular paper). In: European Workshop on Multi-Agent Systems (EUMAS), Paris (France), 16/12/2010-17/12/2010, page (electronic medium), Université Paris Descartes (Décembre 2010), http://www.univ-paris5.fr/

  9. Rosenblatt, F.: Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Technical report, DTIC Document (1961)

    Google Scholar 

  10. Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Prentice hall (2010)

    Google Scholar 

  11. Schilit, B.N., Theimer, M.M.: Disseminating active map information to mobile hosts. IEEE Network 8(5), 22–32 (1994)

    CrossRef  Google Scholar 

  12. Sejnowski, T.J.: Unsupervised learning: foundations of neural computation. The MIT Press (1999)

    Google Scholar 

  13. Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction, vol. 1. Cambridge Univ. Press (1998)

    Google Scholar 

  14. Videau, S., Bernon, C., Glize, P., Uribelarrea, J.L.: Controlling bioprocesses using cooperative self-organizing agents. In: Advances on Practical Applications of Agents and Multiagent Systems, pp. 141–150 (2011)

    Google Scholar 

  15. Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine Learning 8(3), 279–292 (1992)

    MATH  Google Scholar 

  16. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)

    CrossRef  Google Scholar 

  17. Zaidenberg, S.: Apprentissage par renforcement de modeles de contexte pour l’informatique ambiante (October 2009)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Guivarch, V., Camps, V., Péninou, A. (2012). Context Awareness in Ambient Systems by an Adaptive Multi-Agent Approach. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds) Ambient Intelligence. AmI 2012. Lecture Notes in Computer Science, vol 7683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34898-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-34898-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34897-6

  • Online ISBN: 978-3-642-34898-3

  • eBook Packages: Computer ScienceComputer Science (R0)