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A Roadmap to Emotionally Intelligent Creative Virtual Assistants

  • Alexander A. Eidlin
  • Alexei V. Samsonovich
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)

Abstract

Cognitive psychology has accumulated a vast amount of knowledge about human social emotions, emotional appraisals and their usage in decision making. Can an emotional cognitive architecture injected into an artifact make it more “humane”, and therefore, more productive in a variety of creative collaboration paradigms? Here, we argue that the answer is positive. A large number of research projects in the field of digital art that are currently underway could benefit from integration of an emotional architecture component into them. An example is the project Robodanza (a robotic dancer), the functioning of which is based on a hidden Markov model trained by a genetic algorithm, yet lacking deep emotional intelligence. Generalizing on this example, we outline a roadmap to building a variety of useful virtual creative assistants to humans based on an emotionally intelligent cognitive architecture.

Keywords

Cognitive modeling Virtual actor Emotional intelligence Creative assistant Co-robots 

Notes

Acknowledgments

The authors are grateful to Dr. Sergey Misyurin, Professor and Director of ICIS of the National Research Nuclear University “MEPhI”, Moscow, Russian Federation, for useful discussions. Our greatest thanks go to Drs. Ignazio Infantino and Umberto Maniscalco, Researchers at ICAR-CNR, section of Palermo, Italy, who provided us with useful background. This work was supported by the RSF Grant # 15-11-30014.

References

  1. 1.
    Manfre, A., Infantino, I., Vella, F., Gaglio, S.: An automatic system for humanoid dance creation. Biol. Inspir. Cogn. Archit. 15, 1–9 (2016)Google Scholar
  2. 2.
    Samsonovich, A.V.: On a roadmap for the BICA Challenge. Biol. Inspir. Cogn. Archit. 1, 100–107 (2012)Google Scholar
  3. 3.
    McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence. In: Chrisley, R., Begeer, S. (eds.) Artificial Intelligence: Critical Concepts, vol. 2, pp. 44–53. Routledge, London (1955/2000)Google Scholar
  4. 4.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River (1995)zbMATHGoogle Scholar
  5. 5.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  6. 6.
    Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Cogn. Syst. Res. 5, 269–306 (2004)CrossRefGoogle Scholar
  7. 7.
    Hudlicka, E.: Guidelines for designing computational models of emotions. Int. J. Synth. Emot. 2(1), 26–79 (2011)CrossRefGoogle Scholar
  8. 8.
    Marsella, S.C., Gratch, J.: EMA: a process model of appraisal dynamics. Cogn. Syst. Res. 10, 70–90 (2008)CrossRefGoogle Scholar
  9. 9.
    Samsonovich, A.V.: Emotional biologically inspired cognitive architecture. Biol. Inspir. Cogn. Archit. 6, 109–125 (2013)Google Scholar
  10. 10.
    Todd, M., Lee, C., O’Boyle, D.: A sensorimotor theory of temporal tracking and beat induction. Psychol. Res. 66, 26–39 (2002)CrossRefGoogle Scholar
  11. 11.
    Toiviainen, P., Luck, G., Thompson, M.R.: Embodied meter: Hierarchical eigenmodes in music-induced movement. Music Percept. Interdisc. J. 28, 59–70 (2010)CrossRefGoogle Scholar
  12. 12.
    Xia, G., Tay, J., Dannenberg, R., Veloso, M.: Autonomous robot dancing driven by beats and emotions of music. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 205–212. International Foundation for Autonomous Agents and Multiagent Systems (2012)Google Scholar
  13. 13.
    Seo, J.-H., Yang, J.-Y., Kim, J., Kwon, D.-S.: Autonomous humanoid robot dance generation system based on real-time music input. In: RO-MAN 2013, pp. 204–209. IEEE (2013)Google Scholar
  14. 14.
    Augello, A., Infantino, I., Pilato, G., Rizzo, R., Vella, F.: Creativity evaluation in a cognitive architecture. Biol. Inspir. Cogn. Archit. 11, 29–37 (2015)Google Scholar
  15. 15.
    Augello, A., Infantino, I., Pilato, G., Rizzo, R., Vella, F.: Binding representational spaces of colors and emotions for creativity. Biol. Inspir. Cogn. Archit. 5, 64–71 (2013)Google Scholar
  16. 16.
    Bartl, C., Dorner, D.: PSI: a theory of the integration of cognition, emotion and motivation. In: Proceedings of the 2nd European Conference on Cognitive Modelling, pp. 66–73. DTIC Document (1998)Google Scholar
  17. 17.
    Samsonovich, A.V., Nadel, L.: Fundamental principles and mechanisms of the conscious self. Cortex 41(5), 669–689 (2005)CrossRefGoogle Scholar
  18. 18.
    Samsonovich, A.V.: The constructor metacognitive architecture. In: Samsonovich, A.V. (ed.) Biologically Inspired Cognitive Architectures II: Papers from the AAAI Fall Symposium, AAAI Technical Report, vol. FS-09-01, Menlo Park, CA, pp. 124–134. AAAI Press (2009)Google Scholar
  19. 19.
    Samsonovich, A.V.: An approach to building emotional intelligence in artifacts. In: Burgard, W., Konolige, K., Pagnucco, M., Vassos, S. (eds.) Cognitive Robotics: AAAI Technical Report, vol. WS-12-06, Menlo Park, CA, pp. 109–116. The AAAI Press (2012)Google Scholar
  20. 20.
    Samsonovich, A.V.: Modeling social emotions in intelligent agents based on the mental state formalism. In: Raskin, V., Taylor, J.M., Nijholt, A., Ruch, W. (eds.) Artificial Intelligence of Humor: Papers from the AAAI Fall Symposium, AAAI Technical Report, vol. FS-12-02, Menlo Park, CA. AAAI Press (2012)Google Scholar
  21. 21.
    Webots: robot simulator (2017). http://aha.isr.tecnico.ulisboa.pt/. Accessed 14 Jan 2017
  22. 22.
    Salam, H., Celiktutan, O., Hupont, I., Gunes, H., Chetouani, M.: Fully automatic analysis of engagement and its relationship to personality in human-robot interactions. IEEE Access J. 5, 705–721 (2016)CrossRefGoogle Scholar
  23. 23.
    ATR Hiroshi Ishiguro Laboratories, Understanding and Transmitting Human Presence (2017). http://www.geminoid.jp/en/projects.html. Accessed 13 Jan 2017
  24. 24.
    Vilhjálmsson, H.H.: Principal Contributions (2017). http://www.ru.is/~hannes/ru_main_research.html. Accessed 13 Jan 2017
  25. 25.
    AHA. Augmented Human Assistance (2017). http://aha.isr.tecnico.ulisboa.pt/. Accessed 13 Jan 2017

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Cybernetics and BICA Lab, Institute for Cyber Intelligence SystemsNational Research Nuclear University “Moscow Engineering Physics Institute”MoscowRussian Federation
  2. 2.Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA

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