An Emotional-Based Hybrid Application for Human-Agent Societies

  • J. A. RinconEmail author
  • V. Julian
  • C. Carrascosa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 368)


The purpose of this paper is to present an emotional-based application for human-agent societies. This kind of applications are those where virtual agents and humans coexist and interact transparently into a fully integrated environment. Specifically, the paper presents an application where humans are immersed into a system that extracts and analyzes the emotional states of a human group trying to maximize the welfare of that humans by playing the most appropriate music in every moment. This system can be used not only online, calculating the emotional reaction of people in the bar to a new song, but also in simulation, to predict the people’s reaction to changes in music or in the bar layout.


  1. 1.
    Satyanarayanan M (2002) A catalyst for mobile and ubiquitous computing. IEEE Pervasive Comput 1(1):2–5Google Scholar
  2. 2.
    Mangina E, Carbo J, Molina JM (2009) Agent-based ubiquitous computing. Atlantis Press, World Scientific, Amsterdam; ParisGoogle Scholar
  3. 3.
    Han D-M, Lim J-H Smart home energy management system using IEEE 802.15. 4 and zigbee. IEEE Trans Consum Electron 56(3):1403–1410Google Scholar
  4. 4.
    Intille SS (2002) Designing a home of the future. IEEE Pervasive comput 1(2):76–82Google Scholar
  5. 5.
    Billhardt H, Julián V, Corchado JM, Fernández A (2014) An architecture proposal for human-agent societies. In: Highlights of practical applications of heterogeneous multi-agent systems, vol 430, pp 344–357. SpringerGoogle Scholar
  6. 6.
    Hale KS, Stanney KM (2002) Handbook of virtual environments: design, implementation, and applications. Human Factors and Ergonomics. Taylor & FrancisGoogle Scholar
  7. 7.
    Rincon JA, Garcia E, Julian V, Carrascosa C (2014) Developing adaptive agents situated in intelligent virtual environments. In: Hybrid artificial intelligence systems, number 8480 in LNCS, pp 98–109. SpringerGoogle Scholar
  8. 8.
    Becker-Asano C, Wachsmuth I (2010) Affective computing with primary and secondary emotions in a virtual human. Auton Agent Multi Agent Syst 20(1):32–49Google Scholar
  9. 9.
    Jain D, Kobti Z (2011) Simulating the effect of emotional stress on task performance using OCC. In: Advances in Artificial Intelligence, pp 204–209. SpringerGoogle Scholar
  10. 10.
    Ali F, Amin M (2013) The influence of physical environment on emotions, customer satisfaction and behavioural intentions in chinese resort hotel industry. In: KMITL-AGBA Conference Bangkok, pp 15–17Google Scholar
  11. 11.
    Barella A, Ricci A, Boissier O, Carrascosa C (2012) MAM5: Multi-agent model for intelligent virtual environments. In: 10th european workshop on multi-agent systems (EUMAS 2012), pp 16–30Google Scholar
  12. 12.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J comput vision 57(2):137–154Google Scholar
  13. 13.
    Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Computer vision and pattern recognition, 1997. Proceedings., 1997 IEEE computer society conference on, pp 130–136. IEEEGoogle Scholar
  14. 14.
    Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw 8(1):98–113Google Scholar
  15. 15.
    Mehrabian A (1997) Analysis of affiliation-related traits in terms of the PAD temperament model. J Psychol 131(1):101–117Google Scholar
  16. 16.
    Nanty A, Gelin R (2013) Fuzzy controlled PAD emotional state of a NAO robot. In: 2013 conference on technologies and applications of artificial intelligence (TAAI), pp 90–96Google Scholar
  17. 17.
    Richert W, Coelho LP (2013) Building Machine Learning Systems with Python. Packt Publishing, BirminghamGoogle Scholar
  18. 18.
    Holzapfel A, Stylianou Y (2007) A statistical approach to musical genre classification using non-negative matrix factorization. In: Acoustics, speech and signal processing, 2007. ICASSP 2007. IEEE international conference on, vol 2, pp II-693. IEEEGoogle Scholar
  19. 19.
    Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Departamento de Sistemas Informáticos Y Computación (DSIC)Universitat Politècnica de ValènciaValenciaSpain

Personalised recommendations