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Modeling User’s Social Attitude in a Conversational System

Baur, Tobias, Schiller, Dominik, André, Elisabeth·2016·Human–Computer Interaction Series
DOI 10.1007/978-3-319-31413-6_10
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  1. Anderson, K., André, E., Baur, T., Bernardini, S., Chollet, M., Chryssafidou, E., Damian, I., Ennis, C., Egges, A., Gebhard, P., Jones, H., Ochs, M., et al.: The tardis framework: intelligent virtual agents for social coaching in job interviews. In: Proceedings of the Tenth International Conference on Advances in Computer Entertainment Technology (ACE-13). Enschede, The Netherlands, November 2013, LNCS 8253 (2013) · DOI
  2. Batrinca, L.M., Stratou, G., Shapiro, A., Morency, L.P., Scherer, S.: Cicero - towards a multimodal virtual audience platform for public speaking training. In: Aylett, R., Krenn, B., Pelachaud, C., Shimodaira, H. (eds.) Proceedings of 13th International Conference on Intelligent Virtual Agents, IVA 2013, Edinburgh, UK, August 29–31, 2013. Lecture Notes in Computer Science, vol. 8108, pp. 116–128, Springer (2013) · DOI
  3. Baur, T., Damian, I., Gebhard, P., Porayska-Pomsta, K., Andre, E.: A job interview simulation: social cue-based interaction with a virtual character. In: 2013 IEEE/ASE International Conference on Social Computing (SocialCom), pp. 220–227, Washington D.C., USA (2013) · DOI
  4. Baur, T., Damian, I., Lingenfelser, F., Wagner, J., André, E.: Nova: automated analysis of nonverbal signals in social interactions. In: Salah, A., Hung, H., Aran, O., Gunes, H. (eds.) Human Behavior Understanding. LNCS, vol. 8212, pp. 160–171, Springer International Publishing (2013) · DOI
  5. Baur, T., Mehlmann, G., Damian, I., Lingenfelser, F., Wagner, J., Lugrin, B., André, E., Gebhard, P.: Context-aware automated analysis and annotation of social human-agent interactions. ACM Trans. Interact. Intell. Syst. (TiiS) 5(2), 11 (2015)
  6. Beck, J.E.: Engagement tracing: using response times to model student disengagement. In: Looi, C., McCalla, G.I., Bredeweg, B., Breuker, J. (eds.) Artificial Intelligence in Education—Supporting Learning through Intelligent and Socially Informed Technology, Proceedings of the 12th International Conference on Artificial Intelligence in Education, AIED 2005, July 18–22, 2005, Amsterdam, The Netherlands. Frontiers in Artificial Intelligence and Applications, vol. 125, pp. 88–95, IOS Press (2005)
  7. Broekens, J., Heerink, M., Rosendal, H.: Assistive social robots in elderly care: a review. Gerontechnology 8(2) (2009) · DOI
  8. Camurri, A., Volpe, G., De Poli, G., Leman, M.: Communicating expressiveness and affect in multimodal interactive systems. IEEE MultiMedia 12(1) (2005) · DOI
  9. Caridakis, G., Wagner, J., Raouzaiou, A., Lingenfelser, F., Karpouzis, K., André, E.: A cross-cultural, multimodal, affective corpus for gesture expressivity analysis. J. Multimodal User Interfaces 7(1–2), 121–134 (2013) · DOI
  10. Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Model. User-Adap. Inter. 19(3), 267–303 (2009) · DOI · Paper page
  11. Damian, I., Baur, T., André, E.: Investigating social cue-based interaction in digital learning games. In: Proceedings of the 1st International Workshop on Intelligent Digital Games for Empowerment and Inclusion (IDGEI 2013) Held in Conjunction with the 8th Foundations of Digital Games 2013 (FDG), ACM, SASDG Digital Library, Chania, Crete, Greece (2013)
  12. De Carolis, B., Novielli, N.: Recognizing signals of social attitude in interacting with ambient conversational systems. J. Multimodal User Interfaces 8(1), 43–60 (2014) · DOI
  13. D’Mello, S., Chipman, P., Graesser, A.: Posture as a predictor of learner’s affective engagement. In: Proceedings of the 29th Annual Cognitive Science Society, pp. 905–991, Cognitive Science Society (2007)
  14. Eagly, A.H., Chaiken, S.: Attitude structure and function. In: Fiske, S.T., Gilbert, D.T., Lindzey, G. (eds.) The handbook of social psychology, vol. 1, pp. 269–322, 4th edn. McGraw-Hill (1998)
  15. Endraß, B., André, E., Rehm, M., Nakano, Y.I.: Investigating culture-related aspects of behavior for virtual characters. Auton. Agent. Multi-Agent Syst. 27(2), 277–304 (2013) · DOI
  16. Gebhard, P., Mehlmann, G., Kipp, M.: Visual scenemaker: a tool for authoring interactive virtual characters. J. Multimodal User Interfaces 6, 3–11 (2012) · DOI
  17. Gebhard, P., Baur, T., Damian, I., Mehlmann, G., Wagner, J., André, E.: Exploring interaction strategies for virtual characters to induce stress in simulated job interviews. In: Proceedings of AAMAS (2014)
  18. Greenwald, A.G., Banaji, M.R.: Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychol. Rev. 102(1), 4 (1995) · DOI · Paper page
  19. Hoque, M.E., Courgeon, M., Martin, J.C., Mutlu, B., Picard, R.W.: MACH: my automated conversation coach. In: Mattern, F., Santini, S., Canny, J.F., Langheinrich, M., Rekimoto, J. (eds.) The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’13, Zurich, Switzerland, September 8–12, 2013, pp. 697–706, ACM (2013) · DOI
  20. Hung, H., Gatica-Perez, D.: Estimating cohesion in small groups using audio-visual nonverbal behavior. Trans. Multimedia 12(6), 563–575 (2010) · DOI
  21. Kang, S.H., Gratch, J., Sidner, C.L., Artstein, R., Huang, L., Morency, L.P.: Towards building a virtual counselor: modeling nonverbal behavior during intimate self-disclosure. In: van der Hoek, W., Padgham, L., Conitzer, V., Winikoff, M. (eds.) International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, Valencia, Spain, June 4–8, 2012 (3 Volumes), pp. 63–70, IFAAMAS (2012)
  22. Kim, J., André, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008) · DOI
  23. Kleinsmith, A., Bianchi-Berthouze, N.: Form as a cue in the automatic recognition of non-acted affective body expressions. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.C. (eds.) Affective Computing and Intelligent Interaction, LNCS, vol. 6974, pp. 155–164. Springer, Berlin (2011) · DOI
  24. Lingenfelser, F., Wagner, J., André, E., McKeown, G., Curran, W.: An event driven fusion approach for enjoyment recognition in real-time. In: Proceedings of the ACM International Conference on Multimedia, MM’14, pp. 377–386. ACM, New York, NY, USA (2014) · DOI
  25. Mahmoud, M., Robinson, P.: Interpreting hand-over-face gestures. In: D’Mello, S.K., Graesser, A.C., Schuller, B.W., Martin, J. (eds.) Proceedings of Fourth International Conference on Affective Computing and Intelligent Interaction, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Part II. Lecture Notes in Computer Science, vol. 6975, pp. 248–255, Springer (2011) · DOI
  26. Mahmoud, M., Morency, L.P., Robinson, P.: Automatic multimodal descriptors of rhythmic body movement. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 429–436, ACM (2013) · DOI
  27. Mancini, M., Ach, L., Bantegnie, E., Baur, T., Berthouze, N., Datta, D., Ding, Y., Dupont, S., Griffin, H., Lingenfelser, F., Niewiadomski, R., Pelachaud, C., Pietquin, O., Piot, B., Urbain, J., Volpe, G., Wagner, J.: Laugh when you’re winning. In: Rybarczyk, Y., Cardoso, T., Rosas, J., Camarinha-Matos, L. (eds.) Innovative and Creative Developments in Multimodal Interaction Systems, IFIP Advances in Information and Communication Technology, vol. 425, pp. 50–79. Springer, Berlin (2014)
  28. Mehlmann, G., Janowski, K., Baur, T., Häring, M., André, E., Gebhard, P.: Modeling gaze mechanisms for grounding in hri. In: Proceedings of the 21th European Conference on Artificial Intelligence. ECAI 2014, Prague, Czech Republic, August 18–22, 2014, Frontiers in Artificial Intelligence and Applications, pp. 1069–1070. IOS Press Ebooks, Amsterdam, The Netherlands (2014)
  29. Michelet, S., Karp, K., Delaherche, E., Achard, C., Chetouani, M.: Automatic imitation assessment in interaction. Human Behavior Understanding. Lecture Notes in Computer Science, vol. 7559, pp. 161–173. Springer, Berlin (2012) · DOI
  30. Morency, L.P.: Modeling human communication dynamics. IEEE Signal Process. Mag. 27(5), 112–116 (2010) · DOI
  31. Nakano, Y.I., Ishii, R.: Estimating user’s engagement from eye-gaze behaviors in human-agent conversations. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI ’10, pp. 139–148. ACM, New York, NY, USA (2010) · DOI
  32. Niewiadomski, R., Hofmann, J., Urbain, J., Platt, T., Wagner, J., Piot, B., Cakmak, H., Pammi, S., Baur, T., Dupont, S., Geist, M., Lingenfelser, F., McKeown, G., Pietquin, O., Ruch, W.: Laugh-aware virtual agent and its impact on user amusement. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent systems. AAMAS’13, pp. 619–626. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2013)
  33. Pease, A.: Body Language. Sheldon Press, London (1988)
  34. Petridis, S., Gunes, H., Kaltwang, S., Pantic, M.: Static vs. dynamic modeling of human nonverbal behavior from multiple cues and modalities. In: Crowley, J.L., Ivanov, Y.A., Wren, C.R., Gatica-Perez, D., Johnston, M., Stiefelhagen, R. (eds.) Proceedings of the 11th International Conference on Multimodal Interfaces, ICMI 2009, Cambridge, Massachusetts, USA, November 2–4, 2009, pp. 23–30, ACM (2009) · DOI
  35. Reeves, B., Nass, C.: How people treat computers, television, and new media like real people and places. CSLI Publications and Cambridge university press, Cambridge (1996)
  36. Rich, C., Ponsleur, B., Holroyd, A., Sidner, C.L.: Recognizing engagement in human-robot interaction. In: Proceedings of the 5th ACM/IEEE International Conference on Human-robot interaction, HRI’10, pp. 375–382. IEEE Press, Piscataway (2010) · DOI
  37. Russell, S.J., Norvig, P.: Artificial Intelligence: a modern approach, 2nd int. edn. Prentice Hall, Upper Saddle River (2003)
  38. Salam, H., Chetouani, M.: A multi-level context-based modelling of engagement in human-robot interaction. In: International Workshop on Context Based Affect Recognition (2015) · DOI
  39. Sandbach, G., Zafeiriou, S., Pantic, M., Yin, L.: Static and dynamic 3d facial expression recognition: a comprehensive survey. Image Vision Comput. 30(10), 683–697 (2012) · DOI · Paper page
  40. Scherer, S., Marsella, S., Stratou, G., Xu, Y., Morbini, F., Egan, A., Rizzo, A., Morency, L.P.: Perception markup language: Towards a standardized representation of perceived nonverbal behaviors. In: Nakano, Y., Neff, M., Paiva, A., Walker, M. (eds.) Intelligent Virtual Agents, LNCS, vol. 7502, pp. 455–463. Springer, Berlin (2012) · DOI
  41. Sebe, N., Cohen, I., Gevers, T., Huang, T.S.: Emotion recognition based on joint visual and audio cues. In: Proceedings of the 18th International Conference on Pattern Recognition—Volume 01, ICPR’06, pp. 1136–1139. IEEE Computer Society, Washington, DC, USA (2006) · DOI · Paper page
  42. Sidner, C.L., Kidd, C.D., Lee, C., Lesh, N.: Where to look: a study of human-robot engagement. In: IUI ’04: Proceedings of the 9th International Conference on Intelligent user Interfaces, pp. 78–84. ACM Press, New York, NY, USA (2004) · DOI
  43. Traum, D.R., DeVault, D., Lee, J., Wang, Z., Marsella, S.: Incremental dialogue understanding and feedback for multiparty, multimodal conversation. In: Nakano, Y., Neff, M., Paiva, A., Walker, M.A. (eds.) Proceedings of 12th International Conference on Intelligent Virtual Agents, IVA 2012, Santa Cruz, CA, USA, September, 12–14, 2012. Lecture Notes in Computer Science, vol. 7502, pp. 275–288, Springer (2012) · DOI
  44. Vail, A.K., Grafsgaard, J.F., Wiggins, J.B., Lester, J.C., Boyer, K.E.: Predicting learning and engagement in tutorial dialogue: a personality-based model. In: Salah, A.A., Cohn, J.F., Schuller, B.W., Aran, O., Morency, L., Cohen, P.R. (eds.) Proceedings of the 16th International Conference on Multimodal Interaction, ICMI 2014, Istanbul, Turkey, November 12–16, 2014, pp. 255–262, ACM (2014) · DOI
  45. Valstar, M.: Automatic facial expression analysis. In: Mandal, M.K., Awasthi, A. (eds.) Understanding Facial Expressions in Communication, pp. 143–172. Springer India, New York (2015) · DOI
  46. Vogt, T., André, E., Bee, N.: Emovoice—a framework for online recognition of emotions from voice. In: Perception in Multimodal Dialogue Systems, 4th IEEE Tutorial and Research Workshop on Perception and Interactive Technologies for Speech-Based Systems, Kloster Irsee, Germany, LNCS, pp. 188–199, Springer (2008) · DOI
  47. Wagner, J., Lingenfelser, F., Baur, T., Damian, I., Kistler, F., André, E.: The social signal interpretation (ssi) framework—multimodal signal processing and recognition in real-time. In: Proceedings of ACM MULTIMEDIA 2013, Barcelona (2013) · DOI
  48. Whitehill, J., Serpell, Z., Lin, Y., Foster, A., Movellan, J.R.: The faces of engagement: automatic recognition of student engagementfrom facial expressions. T. Affect. Comput. 5(1), 86–98 (2014) · DOI
  49. Yu, C., Aoki, P.M., Woodruff, A.: Detecting User Engagement in Everyday Conversations. eprint arXiv:cs/0410027 (2004) · DOI

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Metadata

Authors
Baur, T., Schiller, D., & André, E.
Venue
Human–Computer Interaction Series
Publisher
Springer International Publishing
Published
2016
Pages
181-199
ISSN
1571-5035
DOI
10.1007/978-3-319-31413-6_10
Cited by
5
References
2
Licence
www.springer.com/tdm (unspecified)

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