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The Self-Adaptive Context Learning Pattern: Overview and Proposal

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9405)

Abstract

Over the years, our research group has designed and developed many self-adaptive multi-agent systems to tackle real-world complex problems, such as robot control and heat engine optimization. A recurrent key feature of these systems is the ability to learn how to handle the context they are plunged in, in other words to map the current state of their perceptions to actions and effects. This paper presents the pattern enabling the dynamic and interactive learning of the mapping between context and actions by our multi-agent systems.

Keywords

  • Self-organisation
  • Context
  • Learning
  • Adaptation
  • Multi-agent system
  • Cooperation
  • Machine learning

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References

  1. Ross Ashby, W.: An Introduction to Cybernetics. Chapman & Hall, London (1956)

    MATH  Google Scholar 

  2. Bazire, M., Brézillon, P.: Understanding context before using it. In: Dey, A.K., Leake, D.B., Kokinov, B., Turner, R. (eds.) CONTEXT 2005. LNCS (LNAI), vol. 3554, pp. 29–40. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  3. Boes, J., Migeon, F., Glize, P., Salvy, E.: Model-free optimization of an engine control unit thanks to self-adaptive multi-agent systems. In: ERTS2, Toulouse, SIA/3AF/SEE, pp. 350–359 (2014)

    Google Scholar 

  4. Bonjean, N., Mefteh, W., Gleizes, M.-P., Maurel, C., Migeon, F.: Adelfe 2.0. In: Cossentino, M., Hilaire, V., Molesini, A., Seidita, V. (eds.) Handbook on Agent-Oriented Design Processes, pp. 19–63. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  5. Brézillon, P.: Context in problem solving: a survey. Knowl. Eng. Rev. 14(01), 47–80 (1999)

    CrossRef  MATH  Google Scholar 

  6. Capera, D., Georgé, J.-P., Gleizes, M.-P., Glize, P.: The amas theory for complex problem solving based on self-organizing cooperative agents. In: Proceedings of the Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, WET ICE 2003, pp. 383–388 (2003)

    Google Scholar 

  7. Chaput, H.H., Kuipers, B., Miikkulainen, R.: Constructivist learning: a neural implementation of the schema mechanism. In: Proceedings of the Workshop on Self-Organizing Maps (WSOM 2003) (2003)

    Google Scholar 

  8. Drescher, G.L.: Made-Up Minds: A Constructivist Approach to Artificial Intelligence. MIT Press, Cambridge (1991)

    MATH  Google Scholar 

  9. Guivarch, V., Camps, V., Péninou, A.: AMADEUS: an adaptive multi-agent system to learn a user’s recurring actions in ambient systems. Adv. Distrib. Comput. Artif. Intell. J., Special Issue 1(3), 1–10 (2012)

    Google Scholar 

  10. Heylighen, F., Bates, J., Maack, M.N.: Encyclopedia of Library and Information Sciences. Taylor & Francis, London (2008)

    Google Scholar 

  11. Kalenka, S.: Modelling social interaction attitudes in multi-agent systems. Ph.D. thesis, Citeseer (2001)

    Google Scholar 

  12. Mazac, S., Armetta, F., Hassas, S.: On bootstrapping sensori-motor patterns for a constructivist learning system in continuous environments. In: Alife 14: Fourteenth International Conference on the Synthesis and Simulation of Living Systems (2014)

    Google Scholar 

  13. Noel, V., Zambonelli, F.: Engineering emergence in multi-agent systems: following the problem organisation. In: 2014 International Conference on High Performance Computing & Simulation (HPCS), pp. 444–451. IEEE (2014)

    Google Scholar 

  14. Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agents Multi-Agent Syst. 11(3), 387–434 (2005)

    CrossRef  Google Scholar 

  15. Perotto, F.S., Vicari, R., Alvares, L.O.: An autonomous intelligent agent architecture based on constructivist AI. In: Bramer, M., Devedzic, V. (eds.) Artificial Intelligence Applications and Innovations. IFIP, vol. 154, pp. 103–115. Springer, New York (2004)

    CrossRef  Google Scholar 

  16. Di Marzo Serugendo, G., Gleizes, M.-P., Karageorgos, A.: Self-organising systems. In: Di Marzo Serugendo, G., Gleizes, M.-P., Karageorgos, A. (eds.) Self-organising Software, pp. 7–32. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  17. Verstaevel, N., Régis, C., Gleizes, M.-P., Robert, F.: Principles and experimentations of self-organizing embedded agents allowing learning from demonstration in ambient robotic. Procedia Comput. Sci. 52, 194–201 (2015). The 6th International Conference on Ambient Systems, Networks and Technologies (ANT 2015)

    CrossRef  Google Scholar 

  18. Verstaevel, N., Régis, C., Guivarch, V., Gleizes, M.-P., Robert, F.: Extreme sensitive robotic a context-aware ubiquitous learning. In: ICAART, INSTICC, vol. 1, pp. 242–248 (2015)

    Google Scholar 

  19. Videau, S., Bernon, C., Glize, P., Uribelarrea, J.-L.: Controlling bioprocesses using cooperative self-organizing agents. In: Demazeau, Y., Pĕchoucĕk, M., Corchado, J.M., Bajo Pérez, J. (eds.) Advances on Practical Applications of Agents and Multiagent Systems. AISC, vol. 88, pp. 141–150. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

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Correspondence to Jérémy Boes .

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Boes, J., Nigon, J., Verstaevel, N., Gleizes, MP., Migeon, F. (2015). The Self-Adaptive Context Learning Pattern: Overview and Proposal. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-25591-0_7

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