Human-Centered Distributed Conversational Modeling: Efficient Modeling of Robust Virtual Human Conversations

  • Brent Rossen
  • Scott Lind
  • Benjamin Lok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5773)


Currently, applications that focus on providing conversations with virtual humans require extensive work to create robust conversational models. We present a new approach called Human-centered Distributed Conversational Modeling. Using this approach, users create conversational models in a distributed manner. To do this, end-users interact with virtual humans to provide new stimuli (questions and statements), and domain-specific experts (e.g. medical/psychology educators) provide new virtual human responses. Using this process, users become the primary developers of conversational models. We tested our approach by creating an example application, Virtual People Factory. Using Virtual People Factory, a pharmacy instructor and 186 pharmacy students were able to create a robust conversational model in 15 hours. This is approximately 10% of the time typical in current approaches and results in more comprehensive coverage of the conversational space. In addition, surveys demonstrate the acceptability of this approach by both educators and students.


Virtual Humans Agents and Intelligent Systems Human-centered Computing Distributed Knowledge Acquisition End-user Programming 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kenny, P., et al.: Building interactive virtual humans for training environments. In: ITSEC 2007, NTSA (2007)Google Scholar
  2. 2.
    Dickerson, R., et al.: Evaluating a Script-Based Approach for Simulating Patient-Doctor Interaction. In: SCS 2005 International Conference on Human-Computer Interface Advances for Modeling and Simulation, pp. 79–84 (2005)Google Scholar
  3. 3.
    Kenny, P., Parsons, T.D., Gratch, J., Rizzo, A.A.: Evaluation of justina: A virtual patient with PTSD. In: Prendinger, H., Lester, J.C., Ishizuka, M. (eds.) IVA 2008. LNCS (LNAI), vol. 5208, pp. 394–408. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Leuski, A., et al.: Building effective question answering characters. In: Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue (2006)Google Scholar
  5. 5.
    Reiter, E., Sripada, S., Robertson, R.: Acquiring correct knowledge for natural language generation. Journal of Artificial Intelligence Research 18, 491–516 (2003)zbMATHGoogle Scholar
  6. 6.
    Singh, P., et al.: Open Mind Common Sense: Knowledge Acquisition from the General Public. In: On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE, pp. 1223–1237 (2002)Google Scholar
  7. 7.
    von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 319–326 (2004)Google Scholar
  8. 8.
    Villaume, W.A., Berger, B.A., Barker, B.N.: Learning Motivational Interviewing: Scripting a Virtual Patient. American Journal of Pharmaceutical Education 70(2) (2006)Google Scholar
  9. 9.
    Ruttkay, Z., et al.: Evaluating Embodied Conversational Agents. Evaluating Embodied Conversational Agents 4121 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Brent Rossen
    • 1
  • Scott Lind
    • 2
  • Benjamin Lok
    • 1
  1. 1.CISEUniversity of FloridaGainesvilleUSA
  2. 2.Dept of Surgery, OncologyMedical College of GeorgiaAugustaUSA

Personalised recommendations