Advertisement

Smart Spaces: A Metacognitive Approach

  • Ekaterina Gilman
  • Jukka Riekki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7096)

Abstract

Smart spaces provide services that support users in their daily lives. This requires the smart spaces to recognize the situations and adapt to them. Identifying the situation and adjustment to it in the physical environment has attracted lots of research, but recognition and adaptation at the meta-level has not been studied much. We refer with meta-level recognition and adaptation, that is, with metacognitive functionality, to evaluating the decisions made by the smart space and to adapting the decision making to maximize user experience. The main objective of this PhD work is to equip smart spaces with metacognitive functionality and the expected main contribution is a general framework for Metacognitive Smart Spaces (MSS).

Keywords

smart space ubiquitous computing metacognition metareasoning reasoning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    McBurney, S., Papadopoulou, E., Taylor, N., Williams, H.: Adapting Pervasive Environments through Machine Learning and Dynamic Personalization. In: International Symposium on Parallel and Distributed Processing with Applications, pp. 395–402. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  2. 2.
    Cox, M.: Metacognition in computation: A selected research review. Artif. Intell. 169(2), 104–141 (2005)CrossRefGoogle Scholar
  3. 3.
    Cox, M., Raja, A.: Metareasoning: a manifesto. In: Metareasoning: Thinking about Thinking workshop held within 23 AAAI Conference on Artificial Intelligence (2008)Google Scholar
  4. 4.
    Schmidt, A., Kranz, M., Holleis, P.: Interacting with the ubiquitous computer: towards embedding interaction. In: The 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies (sOc-EUSAI 2005), pp. 147–152. ACM, New York (2005)Google Scholar
  5. 5.
    Wu, C.-L., Fu, L.-C.: Analysis and evaluation of system integration models for human-system interaction in UbiComp environments. In: The 2nd Conference on Human System Interactions, pp. 672–678. IEEE Computer Society, Washington (2009)Google Scholar
  6. 6.
    Salehie, M., Tahvildari, L.: Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4(2), Article 14, 42 pages (2009)Google Scholar
  7. 7.
    Roman, M., Hess, C., Cerqueira, R., Ranganathan, A., Campbell, R.H., Nahrstedt, K.: A Middleware Infrastructure for Active Spaces. IEEE Pervas. Comput. 1(4), 74–83 (2002)CrossRefGoogle Scholar
  8. 8.
    Kang, K., Song, J., Kim, J., Park, H., Cho, W.-D.: USS Monitor: A Monitoring System for Collaborative Ubiquitous Computing Environment. IEEE T. Consum. Electr. 53(3), 911–916 (2007)CrossRefGoogle Scholar
  9. 9.
    Lee, H.-N., Lim, S.-H., Kim, J.-H.: UMONS: Ubiquitous monitoring system in smart space. IEEE T. Consum. Electr. 55(3), 1056–1064 (2009)CrossRefGoogle Scholar
  10. 10.
    White, M., Jennings, B., Osmani, V., van der Meer, S.: Context driven, user-centric access control for smart spaces. In: The IEE International Workshop on Intelligent Environments, pp. 13–19. Institution of Electrical Engineers, London (2005)Google Scholar
  11. 11.
    Xiang, P., Shi, Y.C.: Resource management based on personal service aggregations in smart spaces. In: Third IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, pp. 39–42. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  12. 12.
    Anderson, M.L., Oates, T.: A Review of Recent Research in Metareasoning and Metalearning. AI Magazine 28(1), 7–16 (2007)Google Scholar
  13. 13.
    Gouin-Vallerand, C., Abdulrazak, B., Giroux, S., Mokhtari, M.: Toward autonomic pervasive computing. In: Tenth International Conference on Information Integration and Web-based Applications and Services, pp. 673–676. ACM, New York (2008)Google Scholar
  14. 14.
    Ahmed, S., Ahmed, S.I., Sharmin, M., Hasan, C.S.: Self-healing for autonomic pervasive computing. In: Vasilakos, A.V., Parashar, M., Karnouskos, S., Pedrycz, W. (eds.) Autonomic Communication, pp. 285–305. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Trumler, W., Petzold, J., Bagci, F., Ungerer, T.: AMUN - autonomic middleware for ubiquitous environments applied to the smart doorplate project. In: International Conference on Autonomic Computing, pp. 274–275. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  16. 16.
    Raja, A., Lesser, V.: Coordinating Agents’ Meta-level Control. In: AAAI 2008 Workshop on Metareasoning: Thinking about Thinking. AAAI Press (2008)Google Scholar
  17. 17.
    Kennedy, C.M.: Decentralized metacognition in context-aware autonomous systems: some key challenges. In: AAAI 2010 Workshop on Metacognition for Robust Social Systems (2010)Google Scholar
  18. 18.
    Cazenave, T.: Metarules to improve tactical Go knowledge. Inf. Sci. Inf. Comput. Sci. 154(3-4), 173–188 (2003)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ekaterina Gilman
    • 1
  • Jukka Riekki
    • 1
  1. 1.Intelligent Systems Group and Infotech OuluUniversity of OuluOuluFinland

Personalised recommendations