Advertisement

Towards Social Care Prediction Services Aided by Multi-agent Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10685)

Abstract

Prediction models are widely used in insurance companies and health services. Even when 120 million people are at risk of suffering poverty or social exclusion in the EU, this kind of models are surprisingly unusual in the field of social services. A fundamental reason for this gap is the difficulty in labeling and annotating social services data. Conditions such as social exclusion require a case-by-case debate. This paper presents a multi-agent architecture that combines semantic web technologies, exploratory data analysis techniques, and supervised machine learning methods. The architecture offers a holistic view of the main challenges involved in labeling data and generating prediction models for social services. Moreover, the proposal discusses to what extent these tasks may be automated by intelligent agents.

Keywords

Multi-agent systems Human-agent societies Social services Machine learning 

Notes

Acknowledgments

This research work is supported by the Spanish Ministry of Economy, Industry and Competitiveness under the R&D project Datos 4.0: Retos y soluciones (TIN2016-78011-C4-4-R, AEI/FEDER, UE).

References

  1. 1.
    European Commission’s DG for Employment, Social Affairs & Inclusion. http://ec.europa.eu/social/main.jsp?catId=751. Accessed Feb 2017
  2. 2.
    Manulife Philippines. Calculate your risk, your partner’s risk or both. http://www.insureright.ca/what-is-your-risk. Accessed Feb 2017
  3. 3.
  4. 4.
    de Oliveira, L.: Fueling the Gold Rush: The Greatest Public Datasets for AI. https://goo.gl/mJO8nf. Accessed Feb 2017
  5. 5.
    Giannella, C., Bhargava, R., Kargupta, H.: Multi-agent systems and distributed data mining. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 1–15. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30104-2_1 CrossRefGoogle Scholar
  6. 6.
    Haron, N.: Poverty and social exclusion around the mediterranean sea. In: Berenger, V., Bresson, F. (eds.) On social exclusion and income poverty in Israel: findings from the European social survey. Economic Studies in Inequality, Social Exclusion and Well-Being, vol. 9, pp. 247–269. Springer, Boston (2013).  https://doi.org/10.1007/978-1-4614-5263-8_9 Google Scholar
  7. 7.
    Kiselev, I., Alhajj, R.: A self-organizing multi-agent system for adaptive continuous unsupervised learning in complex uncertain environments. In: Fox, D., Gomes, C.P. (eds.) Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008), Chicago, 13–17 July 2008, pp. 1808–1809. AAAI Press (2008)Google Scholar
  8. 8.
    Kohavi, R., Becker, B.: Adult data set. https://archive.ics.uci.edu/ml/datasets/Adult. Accessed Feb 2017
  9. 9.
    Lafuente-Lechuga, M., Faura-Martínez, U.: Análisis de los individuos vulnerables a la exclusión social en españa en 2009. Anales de ASEPUMA 21, 3003–3023 (2013)Google Scholar
  10. 10.
    Levitas, R., Pantazis, C., Fahmy, E., Gordon, D., Lloyd, E., Patsios, D.: The Multi-dimensional Analysis of Social Exclusion. Social Exclusion Task Force, Cabinet Office, London (2007)Google Scholar
  11. 11.
    Nwana, H.S.: Software agents: an overview. Knowl. Eng. Rev. 11, 205–244 (1996)CrossRefGoogle Scholar
  12. 12.
    Park, J.-E., Oh, K.-W.: Multi-agent systems for intelligent clustering. Int. J. Comput. Electr. Autom. Control Inf. Eng. 1(11), 275–280 (2007)Google Scholar
  13. 13.
    Ponni, J., Shunmuganathan, K.L.: Multi-agent system for data classification from data mining using SVM. In: 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), pp. 828–832, December 2013Google Scholar
  14. 14.
    Ramos, J., Varela, A.: Beyond the margins: analyzing social exclusion with a homeless client dataset. Soc. Work Soc. 14(2) (2016). http://socwork.net/sws/article/view/27/73
  15. 15.
    Rank, M.R., Hirschl, T.A.: Calculate Your Economic Risk. New york times (2016)Google Scholar
  16. 16.
    Serrano, E., del Pozo-Jiménez, P., Suárez-Figueroa, M.C., González-Pachón, J., Bajo, J., Gómez-Pérez, A.: A multi-agent architecture for labeling data and generating prediction models in the field of social services. In: Bajo, J., et al. (eds.) PAAMS 2017. CCIS, vol. 722, pp. 177–184. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60285-1_15 CrossRefGoogle Scholar
  17. 17.
    Serrano, E., del Pozo-Jiménez, P., Suárez-Figueroa, M.C., González-Pachón, J., Bajo, J., Gómez-Pérez, A.: Predicting the risk of suffering chronic social exclusion with machine learning. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds.) DCAI 2017. AISC, vol. 620, pp. 132–139. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-62410-5_16 Google Scholar
  18. 18.
    Serrano, E., Rovatsos, M., Botía, J.A.: Data mining agent conversations: a qualitative approach to multiagent systems analysis. Inf. Sci. 230, 132–146 (2013)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Suh, E., TiffanyVizard, P., AsgharBurchardt, T.: Quality of life in Europe: social inequalities. In: 3rd European Quality of Life Survey (2013)Google Scholar
  20. 20.
    Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, New York (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ontology Engineering GroupUniversidad Politécnica de MadridMadridSpain

Personalised recommendations