AMADEUS: an adaptive multi-agent system to learn a user’s recurring actions in ambient systems

  • Valérian Guivarch
    Université de Toulouse Valerian.Guivarch[at]
  • Valérie Camps
    Université de Toulouse
  • André Péninou
    Université de Toulouse


Ambient systems are characterized by their dynamics and their huge complexity.An important issue in this field is their capability to provide a relevant behaviour in order to satisfy users involved. Multi-agent systems, because of their ability to deal with dynamic, distributed and not deterministic environments, seem to be very promising to solve adaptation problems in ambient systems. The objective of our study is to propose Amadeus, a system able to learn the user’s behaviour in order to perform his recurrent actions on his behalf, independently of the ambient system in which it is applied. The originality of our contribution is to be generic and to promote a process able to learn at runtime without any prior learning phase and able to filter useful data for characterizing users' context.
  • Referencias
  • Cómo citar
  • Del mismo autor
  • Métricas
G. Cabri, L. Ferrari, L. Leonardi, and F. Zambonelli. The laica project: Supporting ambient intelligence via agents and ad-hoc middleware. Enabling Technologies: Infrastructure for Collaborative Enterprise. 14th IEEE International Workshops on, IEEE, 2005

S. Costantini, L. Mostarda, A. Tocchio, and P. Tsintza. Dalica: Agent-based ambient intelligence for cultural-heritage scenarios. Intelligent Systems, IEEE, 23(2),2008

T. Dujardin, J. Rouillard, J.C. Routier, J.C. Tarby, et al. Gestion intelligente d’un contexte domotique par un système multi-agents. Actes Journées Francophones sur les Systèmes Multi-Agents, 2011

Jean-Pierre Georgée, Marie-Pierre Gleizes, and Valérie Camps. Cooperation. In Giovanna Di Marzo Serugendo, Marie-Pierre Gleizes, and Anthony Karageorgos, editors, Self-organising Software, Natural Computing Series,. Springer Berlin Heidelberg, 2011

T.C. Lech and L.W.M.Wienhofen. AmbieAgents: a scalable infrastructure for mobile and context-aware information services In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, ACM, 2005

S. Lemouzy, V. Camps and P. Glize. Principles and Properties of a MAS Learning Algorithm: a Comparison with Standard Learning Algorithms Applied to Implicit Feedback Assessment. IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2011), Lyon, France, 2011.

Millot, G. Comprendre et réaliser les tests statistiques à l'aide de R Boeck université, Louvain-la-Neuve, Belgique, 1st edition, 2009

S.J. Russell, P. Norvig, J.F. Canny, J.M. Malik, and D.D. Edwards. Articial intelligence: a modern approach. Prentice hall Englewood Cliffs, NJ, 1995

I. Satoh. Mobile agents for ambient intelligence. Proceedings of Massively Multi-Agent Systems, first international workshop MMAS Kyoto, Japan, December 2004

N. Spanoudakis and P. Moraitis. Agent based architecture in an ambient intelligence context. Proceedings of the 4th European Workshop on Multi-Agent Systems (EUMAS'06), Lisbon, Portugal 2006

D.I. Tapia, J. Bajo, J.M. Sanchez, and J.M. Corchado. An ambient intelligence based multi-agent architecture. Developing Ambient Intelligence, 2008.
Guivarch, V., Camps, V., & Péninou, A. (2013). AMADEUS: an adaptive multi-agent system to learn a user’s recurring actions in ambient systems. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 1(3), 1–10.


Download data is not yet available.