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Modelling Device Actions in Smart Environments

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 53))

Abstract

Smart environments are places that contain numerous devices to assist a user. Those devices’ actions can be modelled as planning operators. A problem when modelling such actions is the persistent action problem: Actions are not independent of one another. This is especially relevant when regarding persistent actions: An action that is being executed over a longer timespan may be terminated by a subsequent action that uses the same resources. The question is how to model this adequately. In dynamic environments with a high fluctuation of devices an additional challenge is to solve the persistent action problem with as little global information as possible. In this paper, we introduce two approaches: The first one locks resources which are being used by an action to prevent other actions from using the same resources. The second interleaves planning and execution of actions and is thus able to use software agents as “guards” for actions that are being executed. We furthermore compare the characteristics of both approaches and point out some implications those characteristics have on the modelling and execution of device actions in smart environments.

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References

  1. Amigoni, F., Gatti, N., Pinciroli, C., Roveri, M.: What planner for ambient intelligent applications? IEEE Transactions on Systems, Man and Cybernetics - Part A 35(1), 7–21 (2005)

    Article  Google Scholar 

  2. Bacchus, F., Kabanza, F.: Using Temporal Logic to Control Search in a Forward Chaining Planner. In: Proc. EWSP, pp. 141–153. Press (1995)

    Google Scholar 

  3. Bacchus, F., Kabanza, F.: Planning for temporally extended goals. Annals of Mathematics and Artificial Intelligence 22(1-2), 5–27 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  4. Brooks, R.A.: A robust layered control system for a mobile robot. In: Artificial intelligence at MIT: expanding frontiers, pp. 2–27 (1990)

    Google Scholar 

  5. Chrpa, L.: Linear Logic in Planning. In: Proceedings of Doctoral Consortium of ICAPS, pp. 26–29 (2006)

    Google Scholar 

  6. Doherty, P., Kvarnström, J.: TALplanner: A Temporal Logic-Based Planner. AI Magazine 22(3) (2001)

    Google Scholar 

  7. Heider, T., Kirste, T.: Supporting goal based interaction with dynamic intelligent environments. In: Proc. ECAI, pp. 596–600 (2002)

    Google Scholar 

  8. Issarny, V., Sacchetti, D., Tartanoglu, F., Sailhan, F., Chibout, R., Levy, N., Talamona, A.: Developing ambient intelligence systems: A solution based on web services. Automated Software Engg. 12(1), 101–137 (2005)

    Article  Google Scholar 

  9. Maes, P.: Situated Agents Can Have Goals. In: Maes, P. (ed.) Designing Autonomous Agents, pp. 49–70. MIT Press, Cambridge (1990)

    Google Scholar 

  10. McAllester, D., Rosenblitt, D.: Systematic Nonlinear Planning. In: Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 634–639 (1991)

    Google Scholar 

  11. McDermott, D.: PDDL – The Planning Domain Definition Language. Draft (1998)

    Google Scholar 

  12. Mozer, M.C.: Lessons from an adaptive home. Smart Environments: Technology, Protocols, and Applications, 273–298 (2005)

    Google Scholar 

  13. Reisse, C., Kirste, T.: A Distributed Action Selection Mechanism for Device Cooperation in Smart Environments. In: Proc. IE (2008)

    Google Scholar 

  14. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2003)

    Google Scholar 

  15. Sussman, G.J.: A Computational Model of Skill Acquisition. Technical report, Cambridge, MA, USA (1973)

    Google Scholar 

  16. Weld, D., Etzioni, O.: The first law of robotics (a call to arms). In: Proc. AAAI, pp. 1042–1047 (1994)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Plociennik, C., Burghardt, C., Marquardt, F., Kirste, T., Uhrmacher, A. (2009). Modelling Device Actions in Smart Environments. In: Tavangarian, D., Kirste, T., Timmermann, D., Lucke, U., Versick, D. (eds) Intelligent Interactive Assistance and Mobile Multimedia Computing. IMC 2009. Communications in Computer and Information Science, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10263-9_19

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  • DOI: https://doi.org/10.1007/978-3-642-10263-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10262-2

  • Online ISBN: 978-3-642-10263-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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