Lifelong Machine Learning with Adaptive Multi-Agent Systems

Nicolas Verstaevel, Jérémy Boes, Julien Nigon, Dorian d'Amico, Marie-Pierre Gleizes

Abstract

Sensors and actuators are progressively invading our everyday life as well as industrial processes. They form complex and pervasive systems usually called ”ambient systems” or ”cyber-physical systems”. These systems are supposed to efficiently perform various and dynamic tasks in an ever-changing environment. They need to be able to learn and to self-adapt throughout their life, because designers cannot specify a priori all the interactions and situations they will face. These are strong requirements that push the need for lifelong machine learning, where devices can learn models and behaviours during their whole lifetime and are able to transfer them to perform other tasks. This paper presents a multi-agent approach for lifelong machine learning.

References

  1. Argall, B. D., Chernova, S., Veloso, M., and Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5):469- 483.
  2. Billard, A. (2003). Robota: Clever toy and educational tool. Robotics and Autonomous Systems, 42(3):259-269.
  3. Boes, J., Migeon, F., Glize, P., and Salvy, E. (2014). Model-free Optimization of an Engine Control Unit thanks to Self-Adaptive Multi-Agent Systems (regular paper). In International Conference on Embedded Real Time Software and Systems (ERTS2), Toulouse, 05/02/2014-07/02/2014, pages 350-359. SIA/3AF/SEE.
  4. Boes, J., Nigon, J., Verstaevel, N., Gleizes, M.-P., and Migeon, F. (2015). The self-adaptive context learning pattern: Overview and proposal. In Modeling and Using Context, pages 91-104. Springer.
  5. Georgé, J.-P., Gleizes, M.-P., and Camps, V. (2011). Cooperation. In Di Marzo Serugendo, G., Gleizes, M.-P., and Karageogos, A., editors, Self-organising Software, Natural Computing Series, pages 7-32. Springer Berlin Heidelberg.
  6. Guivarch, V., Camps, V., and Pninou, A. (2012). AMADEUS: an adaptive multi-agent system to learn a user's recurring actions in ambient systems. Advances in Distributed Computing and Artificial Intelligence Journal, Special Issue n3, Special Issue n3(ISSN: 2255-2863):(electronic medium).
  7. Huang, C.-M. (2010). Joint attention in human-robot interaction. Association for the Advancement of Artificial Intelligence.
  8. Jazdi, N. (2014). Cyber physical systems in the context of industry 4.0. In Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on, pages 1-4. IEEE.
  9. Knox, W. B. and Stone, P. (2009). Interactively shaping agents via human reinforcement: The tamer framework. In Proceedings of the Fifth International Conference on Knowledge Capture, pages 9-16. ACM.
  10. Mitchell, T. M. (2006). The discipline of machine learning, volume 9. Carnegie Mellon University, School of Computer Science, Machine Learning Department.
  11. Nehaniv, C. L. and Dautenhahn, K. (2002). The correspondence problem. Imitation in animals and artifacts, 41.
  12. Niewiadomski, R., Hofmann, J., Urbain, J., Platt, T., Wagner, J., Piot, B., Cakmak, H., Pammi, S., Baur, T., Dupont, S., et al. (2013). Laugh-aware virtual agent and its impact on user amusement. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, pages 619-626. International Foundation for Autonomous Agents and Multiagent Systems.
  13. Nigon, J., Gleizes, M.-P., and Migeon, F. (2016a). Selfadaptive model generation for ambient systems. Procedia Computer Science, 83:675-679.
  14. Nigon, J., Glize, E., Dupas, D., Crasnier, F., and Boes, J. (2016b). Use cases of pervasive artificial intelligence for smart cities challenges. In IEEE Workshop on Smart and Sustainable City, Toulouse, juillet.
  15. Pentina, A. and Lampert, C. H. (2015). Lifelong learning with non-iid tasks. In Advances in Neural Information Processing Systems, pages 1540-1548.
  16. Piaget, J. (1962). Play, dreams and imitation in childhood. New York : Norton.
  17. Rifkin, J. (2016). How the third industrial revolution will create a green economy. New Perspectives Quarterly, 33(1):6-10.
  18. Robins, B. and Dautenhahn, K. (2007). Encouraging social interaction skills in children with autism playing with robots. Enfance, 59(1):72-81.
  19. Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., and Edwards, D. D. (2003). Artificial intelligence: a modern approach, volume 2. Prentice hall Upper Saddle River.
  20. Serugendo, G. D. M., Gleizes, M.-P., and Karageorgos, A. (2011). Self-organising systems. In Self-organising Software, pages 7-32. Springer.
  21. Silver, D. L., Yang, Q., and Li, L. (2013). Lifelong machine learning systems: Beyond learning algorithms. In AAAI Spring Symposium: Lifelong Machine Learning, pages 49-55. Citeseer.
  22. Thorisson, K. R., Bieger, J., and Thorarensen, T. (2016). Why artificial intelligence needs a task theory. InArtificial General Intelligence: 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings, volume 9782, page 118. Springer.
  23. Thrun, S. and Mitchell, T. M. (1995). Lifelong robot learning. In The biology and technology of intelligent autonomous agents, pages 165-196. Springer.
  24. Verstaevel, N. (2016). Self-Organization of Robotic Devices Through Demonstrations. Doctoral thesis, Universit de Toulouse, Toulouse, France.
  25. Verstaevel, N., Régis, C., Gleizes, M.-P., and Robert, F. (2016). Principles and experimentations of selforganizing embedded agents allowing learning from demonstration in ambient robotics. Future Generation Computer Systems.
  26. Zhang, B.-T. (2014). Ontogenesis of agency in machines: A multidisciplinary review. In AAAI 2014 Fall Symposium on The Nature of Humans and Machines: A Multidisciplinary Discourse.
Download


Paper Citation


in Harvard Style

Verstaevel N., Boes J., Nigon J., d'Amico D. and Gleizes M. (2017). Lifelong Machine Learning with Adaptive Multi-Agent Systems . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 275-286. DOI: 10.5220/0006247302750286


in Bibtex Style

@conference{icaart17,
author={Nicolas Verstaevel and Jérémy Boes and Julien Nigon and Dorian d'Amico and Marie-Pierre Gleizes},
title={Lifelong Machine Learning with Adaptive Multi-Agent Systems},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={275-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006247302750286},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Lifelong Machine Learning with Adaptive Multi-Agent Systems
SN - 978-989-758-220-2
AU - Verstaevel N.
AU - Boes J.
AU - Nigon J.
AU - d'Amico D.
AU - Gleizes M.
PY - 2017
SP - 275
EP - 286
DO - 10.5220/0006247302750286