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Ambient Intelligence Services Personalization via Social Choice Theory

  • Emilio Serrano
  • Pablo Moncada
  • Mercedes Garijo
  • Carlos A. Iglesias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8867)

Abstract

There are a great number of situations in Ambient Intelligence systems which involve users trying to access shared resources such as: music, TVs, decoration, gym machines, air conditioning, etcetera. The use of Social Choice theory can be employed in these situations to reach consensus while the social welfare is maximized. This paper proposes a multi-agent system to automate these agreements, points out the main challenges in using this system, and quantifies the benefits of its use in a specific case study by an agent-based social simulation.

Keywords

Service customization and personalization Social Choice Ambient Intelligence Agent-based Social Simulation Agreement technologies Multiagent systems 

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References

  1. 1.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing 5(4), 277–298 (2009)CrossRefGoogle Scholar
  2. 2.
    Jennings, N.R.: Agreement technologies. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 111–113. Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Ito, T., Hattori, H., Zhang, M., Matsuo, T.: Rational, Robust, and Secure Negotiations in Multi-Agent Systems. SCI, vol. 89. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  4. 4.
    Arrow, K.J., Sen, A.K., Suzumura, K. (eds.): Handbook of Social Choice and Welfare, 1st edn., vol. 2. Elsevier (2011)Google Scholar
  5. 5.
    Procaccia, A.D.: How is voting theory really useful in multiagent systems? http://www.cs.cmu.edu/~arielpro/papers/vote4mas.pdf
  6. 6.
    Serrano, E., Moncada, P., Garijo, M., Iglesias, C.A.: Evaluating social choice techniques into intelligent environments by agent based social simulation. Information Sciences 286, 102–124 (2014)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Smith, R.G.: The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Transactions on Computers C-29, 1104–1113 (1980)CrossRefGoogle Scholar
  8. 8.
    Aseere, A.M., Gerding, E.H., Millard, D.E.: A voting-based agent system for course selection in e-learning. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 02, pp. 303–310. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  9. 9.
    Woolridge, M., Wooldridge, M.J.: Introduction to Multiagent Systems. John Wiley & Sons, Inc., New York (2001)Google Scholar
  10. 10.
    Mangina, E., Carbo, J., Molina, J.: Agent-Based Ubiquitous Computing. Atlantis Ambient and Pervasive Intelligence. We Publish Books (2010)Google Scholar
  11. 11.
    Nakashima, H., Aghajan, H., Augusto, J.C.: Handbook of Ambient Intelligence and Smart Environments, 1st edn. Springer Publishing Company, Incorporated (2009)Google Scholar
  12. 12.
    Benyoucef, M., Keller, R.K.: An evaluation of formalisms for negotiations in E-commerce. In: Kropf, P.G., Babin, G., Plaice, J., Unger, H. (eds.) DCW 2000. LNCS, vol. 1830, pp. 45–54. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Alcarria, R., Robles, T., Morales, A., López-de Ipiña, D., Aguilera, U.: Enabling flexible and continuous capability invocation in mobile prosumer environments. Sensors 12(7), 8930–8954 (2012)CrossRefGoogle Scholar
  14. 14.
    Chung, J., Gonzalez, G., Armuelles, I., Robles, T., Alcarria, R., Morales, A.: Experiences and challenges in deploying openflow over real wireless mesh networks. IEEE Latin America Transactions (Revista IEEE America Latina) 11(3), 955–961 (2013)CrossRefGoogle Scholar
  15. 15.
    Liu, H.L.H., Darabi, H., Banerjee, P., Liu, J.L.J.: Survey of wireless indoor positioning techniques and systems (2007)Google Scholar
  16. 16.
    San Martín, L.A., Peláez, V.M., González, R., Campos, A., Lobato, V.: Environmental user-preference learning for smart homes: An autonomous approach. J. Ambient Intell. Smart Environ. 2(3), 327–342 (2010)Google Scholar
  17. 17.
    Fip, A.: FIPA ACL Message Structure Specification (SC00061G). FIPA TC Communication (December 2002)Google Scholar
  18. 18.
    Serrano, E., Rovatsos, M., Botía, J.A.: Data mining agent conversations: A qualitative approach to multiagent systems analysis. Information Sciences 230, 132–146 (2013)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Nwana, H.S.: Software agents: An overview. Knowledge Engineering Review 11, 205–244 (1996)CrossRefGoogle Scholar
  20. 20.
    Alcarria, R., Robles, T., Morales, A., Cedeño, E.: Resolving coordination challenges in distributed mobile service executions. International Journal of Web and Grid Services 10(2), 168–191 (2014)CrossRefGoogle Scholar
  21. 21.
    Morales, A., Alcarria, R., Martin, D., Robles, T.: Enhancing evacuation plans with a situation awareness system based on end-user knowledge provision. Sensors 14(6), 11153–11178 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emilio Serrano
    • 1
  • Pablo Moncada
    • 1
  • Mercedes Garijo
    • 1
  • Carlos A. Iglesias
    • 1
  1. 1.Technical University of MadridMadridSpain

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