Towards an Emergent and Autopoietic Approach to Adaptative Chord Generation through Human Interaction

  • Francisco de Paula Barretto
  • Suzete Venturelli
  • Gabriel Gaudencio do Rego
Part of the Communications in Computer and Information Science book series (CCIS, volume 373)

Abstract

This poster describes a transdisciplinary practical-theoretical on-going research, which address on the discussion about the possible applications of Artificial Intelligence (AI) techniques, such as genetic algorithms, which underlie the Maturana and Varela’s autopoietic concept considering the achievement of emergent results as heuristic to creativity. Through human interaction using neuronal bio-feedback it is possible to provide more natural fitness function to such algorithms.

Keywords

autopoiesis emergence bio-feedback creativity genetic algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Maturana, F., Varela, H.: De Máquinas y Seres Vivos. 3rd edn. Editorial Universitaria (1972)Google Scholar
  2. 2.
    Langton, C.: Artificial life. In: Artificial Life: Proceedings of an Interdisciplinary Workshop on Synthesis and Simulation of Living Systems, vol. 4, pp. 1–47 (1989)Google Scholar
  3. 3.
    Luisi, P.: Autopoiesis: a review and a reappraisal. Naturwissenschaften 90(2), 49–59 (2003)Google Scholar
  4. 4.
    Froese, T., Ziemke, T.: Enactive artificial intelligence: investigating the systemic organization of life and mind. Artificial Intelligence 173(4-3), 446–500 (2009)Google Scholar
  5. 5.
    Pfeifer, R., Bongard, J.: How The Body Shapes The Way We Think: a new view on intelligence. The MIT Press (2007)Google Scholar
  6. 6.
    Cariani, P.: Emergence and creativity. Emoção Artificial 4.0, 21–41 (2009)Google Scholar
  7. 7.
    Tramus, M.H., Chen, C.Y.: La funambule virtuelle et quorum sensing, deux installations interactives s’inspirant du connexionnisme et de l’évolutionnisme. La création artistique face aux nouvelles technologies (2005)Google Scholar
  8. 8.
    Holland, J.H.: Adaptation In Natural and Artificial Systems. The University of Michigan Press (1975)Google Scholar
  9. 9.
    Sourina, O., Liu, Y., Nguyen, M.: Real-time eeg-based emotion recognition for music therapy. Journal of Multimodal User Interfaces 5(1-2), 27–35 (2012)CrossRefGoogle Scholar
  10. 10.
    Steinbeis, N., Koelsch, S., Sloboda, J.: The role of harmonic expectancy violations in musical emotions: Evidence from subjective, physiological, and neural responses. Journal of Cognitive Neuroscience 18(8), 1380–1393 (2006)CrossRefGoogle Scholar
  11. 11.
    Liu, Y., Sourina, O.: Eeg-based dominance level recognition for emotion-enabled interaction. In: IEEE International Conference on Multimedia and Expo (2012)Google Scholar
  12. 12.
    Vi, C.T., Subramanian, S.: Detecting error-related negativity for interaction design. In: 30th Conference on Human Factors in Computing Systems, pp. 493–502 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francisco de Paula Barretto
    • 1
  • Suzete Venturelli
    • 1
  • Gabriel Gaudencio do Rego
    • 2
  1. 1.Computer Art Research LabUniversity of BrasiliaFederal DistrictBrazil
  2. 2.Laboratory of Cognitive and Social NeuroscienceMackenzie Presbyterian UniversitySo PauloBrazil

Personalised recommendations