Adaptive service composition in an ambient environment with a multi-agent system

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Abstract

The ambient intelligence consists of the integration of computer science technologies in the objects of the environment around us in order to create a smart one. Each object in the environment performs a different service as part of achieving a task. The adaptive Service composition is a context-aware system used in the situation where the user’s needs cannot be satisfied by a single service but by the combination of several ones. One of the main and principal characteristics of ambient environments is the adaptability to the context. The multi-agent systems with their characteristics of autonomy, proactivity, mobility and sociability are one of the most promising techniques of service composition and they became a major and significant paradigm for developping ambient systems. In this paper, we present a model of adaptive service composition in ambient environment based on multi-agent system named Multi Agent System for Adaptive Service Composition (MAS-ASC). To each object of the environment is associated an agent. The set of the agents coordinate their competences taking into account the context in order to perform an activity. MAS-ASC is based on the Contract Net Protocol in which the agents exchange their proposals in the form of call for proposal. It is implemented on Java Agent DEvelopment framework platform and has been tested to economize the consumption of energy in an intelligent lighting system example.

Keywords

Adaptive service composition Context-aware Ambient intelligence Multi agent system Contract net protocol 
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References

  1. Andriatrimoson A (2012) Assistance robotise la personne en environnement cooprant. Doctoral Dissertation, Universit d’Evry-Val d’EssonneGoogle Scholar
  2. Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2(4):263–277CrossRefGoogle Scholar
  3. Bellifemine FL, Caire G, Greenwood D (2007) Developing multi-agent systems with JADE, vol 7. WileyCrossRefGoogle Scholar
  4. Bellifemine F, Poggi A, Rimassa G (1999) JADEA FIPA-compliant agent framework. In: Proceedings of PAAM, vol 99, No. 97–108, p 33Google Scholar
  5. Benavente Peces C, Ahrens A, Filipe J (2014) Advances in technologies and techniques for ambient intelligence. J Ambient Intell Hum Comput 5(5):621–622CrossRefGoogle Scholar
  6. Chaib A (2011) Composition automatique et semi automatique des services Web semantique Par lutilisation des techniques de planification distribuees Les Systemes Multi Agents. Master Thesis Universite de setif, AlgerieGoogle Scholar
  7. Chaib A, Boussebough I, Chaoui A (2015) Multi-agent system in ambient environment for assistance of elderly sick peoples. In: Proceedings of the international conference on intelligent information processing, security and advanced communication, ACM, p 21Google Scholar
  8. Chaouche AC, Ili JM, Sadouni DE (2016) A Guidance of Ambient Agents Adapted to Opportunistic Situations. In: International symposium on intelligent and distributed computing. Springer International Publishing, pp 47–56Google Scholar
  9. Charif Y (2007) Chorgraphie dynamique de services base sur la coordination d’agents introspectifs. Doctoral Dissertation, Paris, p 6Google Scholar
  10. Chen T, Chan CC, Wu HC, Lin YC (2015) Ambient intelligence and ergonomics in Asia. J Ambient Intell Human Comput 6:1–2Google Scholar
  11. Cook DJ (2009) Multi-agent smart environments. J Ambient Intell Smart Environ 1(1):51–55Google Scholar
  12. Corchado JM, Bajo J, De Paz Y, Tapia DI (2008) Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decis Support Syst 44(2):382–396CrossRefGoogle Scholar
  13. Coutaz J, Crowley J (2008) Plan intelligence ambiante: dfis et opportunits. Document de rflexion conjoint du comit dexperts Informatique Ambiante du dpartement ST2I du CNRS et du Groupe de Travail Intelligence Ambiante du Groupe de Concertation Sectoriel (GCS3) du Ministre de lEnseignement Suprieur et de la Recherche 1Google Scholar
  14. De la Prieta F, Bajo J, Rodríguez S, Corchado JM (2017) MAS-based self-adaptive architecture for controlling and monitoring Cloud platforms. J Ambient Intell Human Comput 8:213–221Google Scholar
  15. Dujardin T, Rouillard J, Routier JC, Tarby JC (2011) Gestion intelligente dun contexte domotique par un Systme Multi-Agents. Universit LilleGoogle Scholar
  16. Ferber J (1995) Multi-agent systems: vers une intelligence collective. Interedition, ReadingMATHGoogle Scholar
  17. Ferrando SP, Onaindia E (2013) Context-aware multi-agent planning in intelligent environments. Inf Sci 227:22–42CrossRefGoogle Scholar
  18. Grisey A, Pommier F, Chantry N, Piasentin J, Chasseriaux G (2007) Utilisation rationnelle de lenergie dans les serres situation technico-economique. Etude realisee pour le compte de l ADEME par le Ctifl, l Astredhor et l INHGoogle Scholar
  19. Gutierrez-Garcia JO, Sim KM (2013) Agent-based cloud service composition. Appl Intell 38(3):436–464CrossRefGoogle Scholar
  20. Hu X, Du W, Spencer B (2011) A multi-agent framework for ambient systems development. Proced Comput Sci 5:82–89CrossRefGoogle Scholar
  21. Landau R (2013) Ambient intelligence for the elderly: hope to age respectfully? Aging Health 9(6):593–600CrossRefGoogle Scholar
  22. Lee J, Ma SP, Lin YY, Lee SJ, Wang YC (2008) Dynamic service composition: a discovery-based approach. Int J Softw Eng Knowl Eng 18(02):199–222CrossRefGoogle Scholar
  23. Moraitis P, Spanoudakis N (2007) Argumentation-based agent interaction in an ambient-intelligence context. IEEE Intell Syst 22(6):84–93CrossRefGoogle Scholar
  24. Olaru A (2011) A context-aware multi-agent system for AmI environments. Teza de doctoratGoogle Scholar
  25. Remagnino P, Hagras H, Monekosso N, Velastin S (2006) Ambient intelligence: a Novel paradigm, p 1Google Scholar
  26. Santofimia MJ, Moya F, Villanueva FJ, Villa D, Lopez JC (2008a) An agent-based approach towards automatic service composition in ambient intelligence. Artif Intell Rev 29(3–4):265–276CrossRefGoogle Scholar
  27. Santofimia MJ, Moya F, Villanueva FJ, Villa D, Lopez JC (2008b) Integration of intelligent agents supporting automatic service composition in ambient intelligence. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology. IEEE Computer Society, vol 02, pp 504–507Google Scholar
  28. Smith RG (1979) A framework for distributed problem solving. In: Proceedings of the 6th international joint conference on artificial intelligence, vol 2. Morgan Kaufmann Publishers Inc., pp 836–841Google Scholar
  29. Weiser M (1993) Some computer science issues in ubiquitous computing. Commun ACM 36(7):75–84CrossRefGoogle Scholar
  30. Zhang Y, Huang GQ, Sun S, Yang T (2014) Multi-agent based real time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment. Comput Ind Eng 76:89–97CrossRefGoogle Scholar
  31. Zhang Y, Qian C, Lv J, Liu Y (2017) Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Trans Ind Inf 13(2):737–747Google Scholar
  32. Zhou J, Riekki J, Ylianttila M (2009) Modeling service composition and exploring its characteristics. In: 2009 congress on services-I. IEEE, pp 446–451Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Aouatef Chaib
    • 1
  • Imane Boussebough
    • 2
  • Allaoua Chaoui
    • 1
  1. 1.MISC LaboratoryUniversity Constantine 2 Abdelhamid MehriConstantineAlgeria
  2. 2.LIRE LaboratoryUniversity Constantine 2 Abdelhamid MehriConstantineAlgeria

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