A Multi-dimensional Model for Task Representation and Allocation in Intelligent Environments

  • Victor Zamudio
  • Vic Callaghan
  • Jeannette Chin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3823)

Abstract

In the future, homes will have numerous intelligent communicating devices, and the user would like to configure and coordinate their actions. Appliances and people in intelligent environments will have some degree of mobility. If the user wants to go from one place to another, using the same community, the agent should be able to generalize the service, trying to build an equivalent collection of coordinating services. This ‘work in progress’ paper addresses this issue by proposing a multi-dimensional model that allows visualistation of devices, temporal relationships, mutual interdependencies and the environment dynamics. The model both offers a simplified means of visualising the task space and the interdependencies together with a means of reasoning about algorithmic solutions to task processing. The work is aimed at supporting research into Pervasive Home Environment Networks (PHEN) which is funded by the UK’s Department of Trade and Industry Next Wave Technologies and Markets programme.

References

  1. 1.
    Haigh, K.Z., Kiff, L.M., Myers, J., Guralnik, V., Geib, C.W., Phelps, J., Wagner, T.: The Independent LifeStyle AssistantTM(I.L.S.A.): AI Lessons Learned. In: The Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI 2004), San Jose, CA, July 25-29, pp. 852–857 (2004)Google Scholar
  2. 2.
    Remagnino, P., Foresti, G.L.: Ambient Intelligence: A New Multidisciplinary Paradigm. IEEE Transactions on Systems, Man and Cybernetics, Part A. 35(1) (January 2005)Google Scholar
  3. 3.
    Gerkey, B.P., Matarić, M.J.: A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems. International Journal of Robotics Research 23(9), 934–954 (2004)CrossRefGoogle Scholar
  4. 4.
    Dudek, G., Jenkin, M., Milios, E.: A Taxonomy for Multi-Agent Robotics. In: Balch, T., Parker, L.E. (eds.) Robot Teams: From Diversity to Polymorphism (2002)Google Scholar
  5. 5.
    Gerkey, B.P., Matarić, M.J.: A Framework for Studying Multi-Robot Task Allocation. In: Schultz, A.C., et al. (eds.) Multi-Robot Systems: From Swarms to Intelligent Automata, the Netherlands, vol. II, pp. 15–26. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  6. 6.
    Chaimowicz, L., Kumar, V., Campos, M.F.M.: A Paradigm for Dynamic Coordination of Multiple Robots. Autonomous Robots 17(1), 7–21 (2004)CrossRefGoogle Scholar
  7. 7.
    Holmquist, L.E., Gellersen, H.W., Kortuem, G., Schmidt, A., Strohbach, M., Antifakos, S., Michahelles, F., Schiele, B., Beigl, M., Maze, R.: Building Intelligent Environments with Smart-Its. IEEE Computer Graphics and Applications 24(1), 56–64 (2004)CrossRefGoogle Scholar
  8. 8.
    Vildjiounaite, E., Malm, E., Kaartinen, J., Alahuhta, P.: Networking of Smart Things in a Smart Home. In: UBIHCISYS 2003 Online Proceedings. UbiCom 2003, Workshop 7 (2003), http://ubihcisys.stanford.edu/online-proceedings/index.html
  9. 9.
    Duman, H., Hagras, H., Callaghan, V.: A Soft-Computing based Approach to Intelligent Association in Agent-Based Ambient-Intelligence Environments. In: Published at 4th. International Conference on Recent Advances in Soft Computing 2002 RASC 2002, Nottingham, U.K (December 2002)Google Scholar
  10. 10.
    Shahi, A., Callaghan, V., Gardner, M.: Introducing Personal Operating Spaces for Ubiquitous Computing Environments. In: Pervasive Mobile Interaction Devices 2005 (PERMID 2005), hosted by 3rd International Conference on Pervasive Computing, Munich, pp. 8–13 (May 2005)Google Scholar
  11. 11.
    Shahi, A., Gardner, M., Callaghan, V.: Supporting Mobile Sessions Across Pervasive Smart Space Environments. In: The IEEE International Workshop on Intelligent Environments, June 28-29. University of Essex (2005)Google Scholar
  12. 12.
    Masuoka, R., Labrou, Y., Song, Z.: Semantic Web and Ubiquitous Computing - Task Computing as an Example - AIS SIGSEMIS Bulletin 1(3) (October 2004)Google Scholar
  13. 13.
    Chin, J., Callaghan, V., Hagras, H., Colley, M., Clarke, G.: End-User Programming in Pervasive Computing Environments. In: The 2005 International Conference on Pervasive Systems and Computing, Las Vegas, Nevada, USA, June 27-30 (2005)Google Scholar
  14. 14.
    Callaghan, V., Colley, M., Hagras, H., Chin, J., Doctor, F., Clarke, G.: Programming iSpaces: A Tale of Two Paradigms. Ch. 24 in book iSpaces published by Springer, Heidelberg (2005)Google Scholar
  15. 15.
    Doctor, F., Hagras, H., Callaghan, V., Lopez, A.: An Adaptive Fuzzy Learning Mechanism for Intelligent Agents in Ubiquitous Computing Environments. In: Proceedings of the 2004 World Automation Conference, Seville, Spain (2004)Google Scholar
  16. 16.
    Rao, S., Cook, D.J.: Predicting Inhabitant Actions Using Action and Task Models with Application to Smart Homes. International Journal of Artificial Intelligence Tools 13(1), 81–100 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Victor Zamudio
    • 1
  • Vic Callaghan
    • 1
  • Jeannette Chin
    • 1
  1. 1.University of EssexColchesterUK

Personalised recommendations