Distributed Embedded Intelligence Room with Multi-agent Cooperative Learning

  • Kevin I-Kai Wang
  • Waleed H. Abdulla
  • Zoran Salcic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4159)


In this paper, a novel Multi-agent control system with fuzzy inference learning and its physical testbed are presented. In the Multi-agent system, distributed controlling, monitoring and cooperative learning are achieved through ubiquitous computing paradigm. The physical testbed named Distributed Embedded Intelligence Room (DEIR) is equipped with a fair amount of embedded devices interconnected in three types of physical networks, namely LonWorks network, RS-485 network and IP network. The changes of environment states and user actions are recorded by software agents and are processed by fuzzy inference learning algorithm to form fuzzy rules that capture user behaviour. With these rules, fuzzy logic controllers can perform user preferred control actions. Comparative analysis shows our control system has achieved noticeable improvement in control accuracy compared to the other offline control system.


Membership Function Fuzzy Rule Cooperative Learn Ambient Intelligence Embed Device 
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  1. 1.
    Ducatel, K., Bogdanowicz, M., Scapolo, F., Burgelman, J.-C.: Scenarios for Ambient Intelligence in 2010. Information Soc. Technol., Advisory Group (ISTAG), Inst. Prospective Technol. Studies (IPTS), Seville (2001)Google Scholar
  2. 2.
    Riva, G., Loreti, P., Lunghi, M., Davide, F.: Presence 2010: The Emergence of Ambient Intelligence. Ios Press, Amsterdam, The Netherlands (2003)Google Scholar
  3. 3.
    Brooks, R.A.: The Intelligent Room Project. In: Second International Conference on Cognitive Technology, pp. 271–278 (1997)Google Scholar
  4. 4.
    Philips, B.A.: Metaglue: A Programming Language for Multi-Agent Systems. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (1999)Google Scholar
  5. 5.
    Gajos, K.: A Knowledge-Based Resource Management System For The Intelligent Room. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2000)Google Scholar
  6. 6.
    Kulkarni, A.A.: A Reactive Behavioral System for the Intelligent Room. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2002)Google Scholar
  7. 7.
    Hagras, H., Colley, M., Callaghan, V., Clarke, G., Duman, H., Holmes, A.: A Fuzzy Incremental Synchronous Learning Technique for Embedded-Agents Learning and Control in Intelligent Inhabited Environments. In: Proc. of the 2002 IEEE Int. Conf. on Fuzzy Syst., pp. 139–144 (2002)Google Scholar
  8. 8.
    Doctor, F., Hagras, H., Callaghan, V.: A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environment. IEEE Trans. Sys. Man Cybern. 35(1), 55–65 (2005)CrossRefGoogle Scholar
  9. 9.
    Rutishauser, U., Schaefer, A.: Adaptive Building Intelligence: A multi-Agent approach. Diploma thesis, University of Applied Science Rapperswil, Switzerland and Institute of Neuroinformatics, Swiss Federal Institute of Technology and University of Zurich, Switzerland (2002)Google Scholar
  10. 10.
    Brumitt, B., Cadiz, J.J.: Let There Be Light! Comparing Interfaces for Homes of the Future. Microsoft Research, Redmond, WA 98052, MSR-TR-2000-92 (2000)Google Scholar
  11. 11.
    Yoshihama, S., Chou, P., Wong, D.: Managing Behavior of Intelligent Environments. In: Proc. of the First IEEE Int. Conf. on Pervasive Comp. and Communications, pp. 330–337 (2003)Google Scholar
  12. 12.
    Tsai, C.F., Wu, H.C.: MASSIHN: A Multi-Agent Architecture for Intelligent Home Network Service. IEEE Trans. on Consumer Electronics 46, 505–514 (2002)CrossRefGoogle Scholar
  13. 13.
    Echelon Corporation, LonWorks Overview, (February 2006),
  14. 14.
    Wang, K.I., Abdulla, W.H., Salcic, Z.: A Multi-Agent System for Intelligent Environments using JADE. In: IEE Int. Workshop on Intell. Environ., pp. 86–91 (2005)Google Scholar
  15. 15.
    Castellano, G., Fanelli, A.M., Mencar, C.: Generation of interpretable fuzzy granules by a double clustering technique. Arch. Contr. Sci. 12(4), 397–410 (2002)zbMATHMathSciNetGoogle Scholar
  16. 16.
    Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)zbMATHGoogle Scholar
  17. 17.
    Jalin, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
  18. 18.
    Wang, L.X.: The MW method completed: A flexible system approach to data minig. IEEE Trans. Fuzzy Syst. 11(6), 678–782 (2003)Google Scholar
  19. 19.
    Wang, L.X., Mendel, J.M.: Generating fuzzy rule by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kevin I-Kai Wang
    • 1
  • Waleed H. Abdulla
    • 1
  • Zoran Salcic
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand

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