Adding real data to detect emotions by means of smart resource artifacts in MAS

  1. RINCÓN, Jaime 1
  2. POZA, Jose Luis 1
  3. POSADAS, Juan Luis 1
  4. JULIÁN, Vicente 1
  5. CARRASCOSA, Carlos 1
  1. 1 Valencia Polytechnic University
Zeitschrift:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Datum der Publikation: 2016

Ausgabe: 5

Nummer: 4

Seiten: 85-92

Art: Artikel

DOI: 10.14201/ADCAIJ2016548592 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Ziele für nachhaltige Entwicklung

Zusammenfassung

This article proposes an application of a social emotional model, which allows to extract, analyse, represent and manage the social emotion of a group of entities. Specifically, the application is based on how music can influence in a positive or negative way over emotional states. The proposed approach employs the JaCalIVE framework, which facilitates the development of this kind of environments. A physical device called smart resource offers to agents processed sensor data as a service. So that, agents obtain real data from a smart resource. MAS uses the smart resource as an artifact by means of a specific communications protocol. The framework includes a design method and a physical simulator. In this way, the social emotional model allows the creation of simulations over JaCalIVE, in which the emotional states are used in the decision-making of the agents.

Bibliographische Referenzen

  • Barella, A., Ricci, A., Boissier, O., and Carrascosa, C., 2012. MAM5: Multi-Agent Model For Intelligent Virtual Environments. In 10th European Workshop on Multi-Agent Systems (EUMAS 2012), pages 16–30.
  • Canento, F., Fred, A., Silva, H., Gamboa, H., and Lourenço, A., 2011. Multimodal biosignal sensor data handling for emotion recognition. In Sensors, 2011 IEEE, pages 647–650. IEEE. https://doi.org/10.1109/icsens.2011.6127029
  • Carter, C. S. and Porges, S. W., 2012. The biochemistry of love: an oxytocin hypothesis. EMBO reports, 14(1):12–16. ISSN 1469-221X. https://doi.org/10.1038/embor.2012.191
  • Colby, B. N., Ortony, A., Clore, G. L., and Collins, A., 1989. The Cognitive Structure of Emotions, volume 18. Cambridge University Press. ISBN 9780521386647. https://doi.org/10.2307/2074241
  • Coulson, M., 2004. Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence. Journal of nonverbal behavior, 28(2):117–139. https://doi.org/10.1023/B:JONB.0000023655.25550.be
  • Haag, A., Goronzy, S., Schaich, P., and Williams, J., 2004. Emotion recognition using bio-sensors: First steps towards an automatic system. In ADS, pages 36–48. Springer. https://doi.org/10.1007/978-3-540-24842-2_4
  • Kim, J. and André, E., 2009. Fusion of multichannel biosignals towards automatic emotion recognition. In Multisensor Fusion and Integration for Intelligent Systems, pages 55–68. Springer. https://doi.org/10.1007/978-3-540-89859-7_5
  • Koelstra, S., Mühl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., and Patras, I., 2012. DEAP: A database for emotion analysis; Using physiological signals. IEEE Transactions on Affective Computing, 3(1):18–31. ISSN 19493045. https://doi.org/10.1109/T-AFFC.2011.15
  • Liu, Y., Sourina, O., and Nguyen, M. K., 2011. Real-time EEG-based Emotion Recognition and its Applications. In Transactions on Computational Science XII, volume 6670, pages 256–277. Springer. ISBN 978-3-642-22335-8. https://doi.org/10.1007/978-3-642-22336-5
  • Mehrabian, a., 1997. Analysis of affiliation-related traits in terms of the PAD Temperament Model. The Journal of psychology, 131(1):101–117. ISSN 0022-3980. https://doi.org/10.1080/00223989709603508
  • Meijer, G. C. M., Meijer, C. M., and Meijer, C. M., 2008. Smart sensor systems. Wiley Online Library. https://doi.org/10.1002/9780470866931
  • Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., and Noguera, J. F. B., 2015. Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8):18080–18101. https://doi.org/10.3390/s150818080
  • Richardson, L., Amundsen, M., and Ruby, S., 2013. RESTful Web APIs. " O'Reilly Media, Inc.".
  • Rincon, J., Garcia, E., Julian, V., and Carrascosa, C., 2014. Developing Adaptive Agents Situated in Intelligent Virtual Environments. In International Conference on Hybrid Artificial Intelligence Systems, pages 98–109. Springer. ISBN 978-3-319-07617-1. https://doi.org/10.1007/978-3-319-07617-1_9
  • Rincon, J., Julian, V., and Carrascosa, C., 2015a. An Emotional-based Hybrid Application for Human-Agent Societies. In 10th Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, volume 368, pages 203–214. ISBN 978-3-319-19718-0. https://doi.org/10.1007/978-3-319-19719-7_18
  • Rincon, J., Julian, V., and Carrascosa, C., 2015b. Social Emotional Model. In 13th International Conference on Practical Applications of Agents and Multi-Agent Systems, volume 9086 of LNAI, pages 199–210. ISBN 978-3-319-18943-7. https://doi.org/10.1007/978-3-319-18944-4_17
  • Sun, Y., Sebe, N., Lew, M. S., and Gevers, T., 2004. Authentic emotion detection in real-time video. In Human Computer Interaction, European Conference on Computer Vision, pages 94–104. Springer. ISBN 3-540-22012-7.
  • Whitman, B. and Smaragdis, P., 2002. Combining Musical and Cultural Features for Intelligent Style Detection. In Ismir, pages 5–10. Paris, France. ISBN 2844261663. ISSN 2844261663.
  • Zhao, Q., 2013. A Molecular and Biophysical Model of the Biosignal. Quantum Matter, 2(1):9–16. ISSN 21647615. doi:10.1166/qm.2013.1017. https://doi.org/10.1166/qm.2013.1017