Skip to main content

Advertisement

SpringerLink
Log in

An Adaptive Genetic-Based Incremental Architecture for the On-Line Coordination of Embedded Agents

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

This paper proposes a novel embedded agent architecture that aims to coordinate a system of interacting embedded agents in real-world intelligent environments using a unique on-line multi-objective and multi-constraint genetic algorithm. The embedded agents can be complex ones such as mobile robots that would operate hierarchical fuzzy logic controllers or simple ones such as desk lamps that would bear threshold functions instead. The architecture would enable the agents to learn the users’ desires and act in real time without the users having to repeatedly configure the system. The multi-embedded-agent system can adapt on-line to handle sudden changes such as unreliable sensors and actuators as well as agents that break down or come into the system. Multifarious experiments were performed on implementations of the aforementioned architecture where the system was tested in different scenarios of varying circumstances, and most importantly on mobile robots and embedded agents in the iDorm—a smart room at the University of Essex.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39

Similar content being viewed by others

References

  1. Hagras H, Callaghan V, Colley M, Clarke G, Pounds-Cornish A, Duman H. A fuzzy logic based embedded agent approach to ambient intelligence in ubiquitous computing environments. IEEE Intell Syst. 2004;19(6):12–20.

    Article  Google Scholar 

  2. Carmena J, Hallam J. Improving performance in a multi-robot task through minimal communication. In: Proceedings of the 7th symposium on intelligent robotic systems (SIRS). Coimbra, Portugal; 1999. p. 329–37.

  3. Saffiotti A. Fuzzy logic in autonomous robotics: behaviour coordination. Artif Intell. 1997;1(4):180–97.

    Google Scholar 

  4. Qu Y, Beale S. A constraint-based model for cooperative response generation in information dialogues. In: Proceedings of AAAI/IAAI. 1999. p. 148–55.

  5. Deb K. An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng. 1998;186(2–4):311–38.

    Google Scholar 

  6. The Universal Plug and Play Forum. UPnP device architecture 1.0. http://www.upnp.org. Accessed 20 July 2006.

  7. Zamudio V, Callaghan V. A topologically-driven strategy to prevent instability in rule-based autonomous agents. In: Proceedings of the 3rd IET international conference on intelligent environments (IE07). 2007.

  8. Bădică C, Bădică A. Solving combinatorial optimisation problems using simulated annealing. In: Proceedings of the ACM/SIGAPP symposium on applied computing: technological challenges of the 1990’s. Kansas City, Missouri, United States: ACM Press; 1992. p. 1031–8.

  9. Dorigo M, Maniezzo V, Colorni A. The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B. 1996;26(1):29–41.

    Article  CAS  Google Scholar 

  10. Dorigo M, Di Caro G. Ant algorithms for discrete optimization. Artif Life. 1999;5(2):137–72.

    Article  CAS  PubMed  Google Scholar 

  11. Mozer M. The neural network house: an environment that adapts to its inhabitants. In: Coen M, editor. Proceedings of the American association for artificial intelligence spring symposium on intelligent environments. Menlo Park, CA: AAAI Press; 1998. p. 110–4.

    Google Scholar 

  12. Kasabov N. Evolving connectionist systems for fast identification, classification, and decision making. Aust J Intell Inf Process Syst. 1999.

  13. Watts M, Kasabov N. Neuro-genetic information processing for optimisation and adaptation in intelligent systems. In: Kasabov N, Kozma R, editors. Neuro-fuzzy techniques for intelligent information processing, Chapter 6. Heidelberg, Germany: Physica Verlag; 1999. p. 97–110.

  14. Gao Y, Er M. Adaptive fuzzy neural control of multiple-link robot manipulators. Special issue on computational intelligence. Int J Rob Autom. 2001;16(4):172–82.

    Google Scholar 

  15. Kasabov N. Evolving fuzzy neural networks for supervised/unsupervised on-line, knowledge-based learning. IEEE Trans Syst Man Cybern B Cybern. 2001;31(6):902–18.

    Article  CAS  PubMed  Google Scholar 

  16. Kasabov N, Kim J, Kozma R, Cohen T. Rule extraction from fuzzy neural networks FuNN: methods and real-world applications. J Adv Comput Intell Intell Inf (JACIII). 2001;5(4):193–200.

    Google Scholar 

  17. Kasabov N, Song Q. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst. 2002;10(2):114–54.

    Article  Google Scholar 

  18. Angelov P, Filev D. An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern B Cybern. 2004;34(1):484–98.

    Article  PubMed  Google Scholar 

  19. Rong HJ, Sudararajan N, Huang GB, Saratchandran P. Sequential adaptive fuzzy interface system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst. 2006;157(9):1260–75.

    Article  Google Scholar 

  20. Goldberg D. Genetic algorithms in search, optimisation and machine learning. Reading, MA: Addison-Wesley; 1989.

    Google Scholar 

  21. Deb K, Agrawal S, Pratap A, Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In: Proceedings of the parallel problem solving from nature VI conference, vol. 16–20. Paris, France; 2000. p. 849–58.

  22. Mozer M, Miller D. Parsing the stream of time: the value of event-based segmentation in a complex, real-world control problem. In: Giles C, Gori M, editors. Adaptive processing of temporal information. Berlin Germany: Springer Verlag; 1998. p. 370–88.

    Google Scholar 

  23. Tawil E, Hagras H. A novel multi-objective multi-constraint genetic algorithms approach for coordinating embedded agents. In: Proceedings of the 2004 international conference on systems, man and cybernetics. The Hague, The Netherlands; 2004. p. 10–3.

  24. Tawil E, Hagras H. Adaptive on-line coordination of ubiquitous computing devices with multiple objectives and constraints. In: Proceedings of the IEE international workshop on intelligent environments. Colchester, UK; 2005. p. 116–24.

Download references

Acknowledgements

Many thanks go to Robin Dowling and Malcolm Lear for their technical support and maintenance of the Brooker Laboratory, mobile robots and μDorms; and Arran Holmes and Gregory Willatt for the maintenance of the iDorm (The Intelligent Dormitory) as well as Faiyaz Doctor, Victor Manuel Zamudio Rodriguez and Lizette Zazueta for their participation in the experiments which were carried out in the iDorm.

Author information

Authors and Affiliations

  1. The Computational Intelligence Centre, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4-3SQ, UK

    Elias Tawil & Hani Hagras

Authors
  1. Elias Tawil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hani Hagras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elias Tawil.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tawil, E., Hagras, H. An Adaptive Genetic-Based Incremental Architecture for the On-Line Coordination of Embedded Agents. Cogn Comput 1, 300–326 (2009). https://doi.org/10.1007/s12559-009-9022-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-009-9022-y

Keywords

Search

Navigation