Today’s society increasingly depends on software systems subject to varying environmental conditions imposing that they continuously adapt. A dynamic adaptation reconfigures a running system from a consistent state into another consistent state. To achieve this goal, a reconfiguration consists in executing a set of actions leading from source to target configuration. The planning of actions has often been neglected in adaptation mechanisms, leading to naive sequential schedules statically predefined. EnTiMid, a ubiquitous software system for assisted living, is one of these adapting systems using basic adaptation plan. This situation may cause problems when considering adaptations involving large set of actions and/or devices, particularly for distributed service-based applications. We propose a framework to ease the integration of different planning algorithms that produce more efficient adaptation plan than an ad-hoc algorithm.


Planning Phase Planning Algorithm Adaptation Plan Dynamic Adaptation Assisted Living 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blum, A.L., Furst, M.L.: Fast Planning Through Planning Graph Analysis. Artificial Intelligence 90, 1636–1642 (1995)Google Scholar
  2. 2.
    Deelman, E., Singh, G., Su, M.H., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Berriman, G.B., Good, J., et al.: Pegasus: A framework for mapping complex scientific workflows onto distributed systems. Scientific Programming 13(3), 219–237 (2005)CrossRefGoogle Scholar
  3. 3.
    Fikes, R., Nilsson, N.J.: STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3/4), 189–208 (1971)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ghallab, M., Isi, C.K., Penberthy, S., Smith, D.E., Sun, Y., Weld, D.: PDDL - The Planning Domain Definition Language. Technical report, CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control (1998)Google Scholar
  5. 5.
    Hallsteinsen, S., Hinchey, M., Park, S., Schmid, K.: Dynamic Software Product Lines. IEEE Computer 41(4) (April 2008)Google Scholar
  6. 6.
    Kephart, J.O., Chess, D.M.: The Vision of Autonomic Computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kichkaylo, T., Ivan, A., Karamcheti, V.: Constrained component deployment in wide-area networks using AI planning techniques. In: Intl. Parallel and Distributed Processing Symposium (2003)Google Scholar
  8. 8.
    Morin, B., Barais, O., Jézéquel, J.-M., Fleurey, F., Solberg, A.: Models@ Run.time to Support Dynamic Adaptation. time to Support Dynamic Adaptation. Computer 42(10), 44–51 (2009)Google Scholar
  9. 9.
    Morin, B., Barais, O., Nain, G., Jézéquel, J.-M.: Taming Dynamically Adaptive Systems with Models and Aspects. In: 31st International Conference on Software Engineering (ICSE 2009), Vancouver, Canada (May 2009)Google Scholar
  10. 10.
    Nain, G., Daubert, E., Barais, O., Jézéquel, J.-M.: Using MDE to Build a Schizofrenic Middleware for Home/Building Automation. In: Mähönen, P., Pohl, K., Priol, T. (eds.) ServiceWave 2008. LNCS, vol. 5377, pp. 49–61. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Francoise André
    • 2
  • Erwan Daubert
    • 1
  • Grégory Nain
    • 1
  • Brice Morin
    • 1
  • Olivier Barais
    • 2
  1. 1.INRIA, Centre Rennes - Bretagne AtlantiqueRennesFrance
  2. 2.University of Rennes1, IRISARennesFrance

Personalised recommendations