Adding real data to detect emotions by means of smart resource artifacts in MAS
Abstract 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.
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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
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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
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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
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
Rincón, J., Poza, J. L., Posadas, J. L., Julián, V., & Carrascosa, C. (2016). Adding real data to detect emotions by means of smart resource artifacts in MAS. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 5(4), 85–92. https://doi.org/10.14201/ADCAIJ2016548592
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