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Improving Smart Environments with Knowledge Ecosystems

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Abstract

This paper presents a distributed cognitive architecture suitable for Ambient Intelligence applications. The key idea is to model an intelligent space as an ecosystem composed by artificial entities which collaborate with each other to perform an intelligent multi-sensor data fusion of both numerical and symbolic information. The semantics associated with the knowledge representation can be used to aid intelligent systems or human supervisors to take decisions according to situations and events occurring within the intelligent space. Experimental results are presented showing how this approach has been successfully applied to smart environments for elderly and disabled.

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References

  1. Barger, T.S., Brown, D.E., Alwan, M.: Health-Status Monitoring Through Analysis of Behavioral Patterns. IEEE Trans. on Systems, Man, and Cybernetics – Part A 35(1) (2005)

    Google Scholar 

  2. Kong, F.T., Chen, Y.-P., Xie, J.-M., Zhou, Z.-D.: Distributed Temperature Control System Based on Multi-sensor Data Fusion. In: Proc. of the 2005 Int. Conf. on Machine Learning and Cybernetics, China (2005)

    Google Scholar 

  3. Chen, S., Bao, H., Zeng, X., Yang, Y.: A Fire Detecting Method based on Multi-sensor Data Fusion. In: Proc. of the 2003 Int. Conf. on System, Man and Cybernetics (SMC), Washington, DC (2003)

    Google Scholar 

  4. Augusto, J.C., Nugent, C.D.: A New Architecture for Smart Homes Based on ADB and Temporal Reasoning. In: Proc. of 3rd Int. Conf. on Smart Homes and Health Telematics, Canada (2005)

    Google Scholar 

  5. White, F.E.: Managing Data Fusion Systems in Joint and Coalition Warfare. In: Proc. of the 1998 Int. Conf. On Data Fusion (EuroFusion98), Great Malvern, UK (October 1998)

    Google Scholar 

  6. Al Dhaher, A.H.G., MacKesy, D.: Multi-sensor Data Fusion Architecture. In: Proc. of the 3rd IEEE Int. Work. On Haptic, Audio and Visual Environments and their Applications (HAVE), Ottawa, Canada (2004)

    Google Scholar 

  7. Hagras, H., Callaghan, V., Colley, M., Graham, C.: A Hierarchical Fuzzy-genetic Multi-agent Architecture for Intelligent Buildings Online Learning, Adaptation and Control. Journal of Information Sciences 150 (1–2) (2003)

    Google Scholar 

  8. Odum, E.P.: Fundamentals of Ecology, W.B. Sanders, USA (1959)

    Google Scholar 

  9. Harnad, S.: The Symbol Grounding Problem. Physica D 42 (1990)

    Google Scholar 

  10. Piaggio, M., Sgorbissa, A., Zaccaria, R.: Pre-emptive versus Non Pre-emptive Real Time Scheduling in Intelligent Mobile Robotics. Journal of Exp. and Theor. Artificial Intelligence 12(2) (2000)

    Google Scholar 

  11. Liao, L., Fox, D., Kautz, H.: Location-based Activity Recognition using Relational Markov Networks. In: Proc. of the 19th Int. Joint Conf. on Artificial Intelligence (IJCAI-05), Edinburg, Scotland (2005)

    Google Scholar 

  12. Makarenko, A., Durrant-Whyte, H.: Decentralized Bayesian Algorithms for Active Sensor Networks. Information Fusion 7, 418–433 (2005)

    Article  Google Scholar 

  13. Levesque, H.J., Branchman, R.J.: Expressiveness and Tractability in Knowledge Representation and Reasoning. Computational Intelligence 3(2) (1987)

    Google Scholar 

  14. Smolensky, P.: Computational Levels and Integrated Connectionist/Symbolic Explanation. In: Smolensky, P., Legendre, G. (eds.) The Harmonic Mind: From Neural Computation to Optimality-Theoretic Grammars, MIT Press, Cambridge, MA (2006)

    Google Scholar 

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Bruno Apolloni Robert J. Howlett Lakhmi Jain

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© 2007 Springer-Verlag Berlin Heidelberg

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Mastrogiovanni, F., Sgorbissa, A., Zaccaria, R. (2007). Improving Smart Environments with Knowledge Ecosystems. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_82

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  • DOI: https://doi.org/10.1007/978-3-540-74829-8_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74828-1

  • Online ISBN: 978-3-540-74829-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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