Journal of Intelligent Information Systems

, Volume 37, Issue 1, pp 23–38 | Cite as

Active adaptation in human-agent collaborative interaction

  • Yong Xu
  • Yoshimasa Ohmoto
  • Kazuhiro Ueda
  • Takanori Komatsu
  • Takeshi Okadome
  • Koji Kamei
  • Shogo Okada
  • Yasuyuki Sumi
  • Toyoaki Nishida
Article

Abstract

When a human user interacts with an adaptive agent to achieve human-agent collaboration, active adaptation is considered to be one of the critical characteristics of the agent. In order to investigate the principal features of active adaptation, we developed a human-agent collaborative experimental environment called WAITER (waiter agent interactive training experimental restaurant) and conducted two types of experiments, a Wizard of OZ (WOZ) agent experiment and an autonomous agent experiment. The objective of these experiments is to observe how human users perceive the agents and change their instructions when interacting with adaptive agents. The results indicate that humans can recognize changes in the agent’s actions and change their instruction methods accordingly. This implies that active adaptation of the agents may encourage the adaptation of the human users and help to build an adaptation loop between them. The experimental results also suggest that active adaptation may play an important role in a human-agent collaborative task.

Keywords

Active adaptation Human-agent collaboration Wizard of OZ agent Autonomous agent Mutual adaptation 

References

  1. Bishop, C. M. (2007). Pattern recognition and machine learning (information science and statistics). Springer.Google Scholar
  2. Cooper, G. F. (1995). A Bayesian method for learning belief networks that contain hidden variables. Journal of Intelligent Information Systems, 4(1), 71–88.CrossRefGoogle Scholar
  3. Goldman, C. V., Rosenschein, J. S., & Rosenschein, J. S. (1996). Incremental and mutual adaptation in multiagent systems. Tech. rep., Institute of Computer Science, The Hebrew University.Google Scholar
  4. Kaplan, F., Oudeyer, P. Y., Kubinyi, E., & Miklosi, A. (2002). Robotic clicker training. Robotics and Autonomous Systems, 38(3–4), 197–206.CrossRefGoogle Scholar
  5. Komatsu, T., Utsunomiya, A., Suzuki, K., Ueda, K., Hiraki, K., & Oka, N. (2005). Experiments toward a mutual adaptive speech interface that adopts the cognitive features humans use for communication and induces and exploits users’ adaptation. International Journal of Human-Computer Interaction, 18(3), 243–268.CrossRefGoogle Scholar
  6. Maes, P. (1994). Modeling adaptive autonomous agents. Artificial Life, 1(1–2), 135–162.Google Scholar
  7. MathWorks Inc. (2010). Creating a graphical user interface. Website: http://www.mathworks.com/demos/matlab/creating-a-graphical-user-interface-matlab-video-tutorial.html. Accessed 1 August 2010.
  8. Silvia, S., & Analia, A. (2005). An interface agent approach to personalize users’ interaction with databases. Journal of Intelligent Information Systems, 25(3), 251–273.CrossRefGoogle Scholar
  9. Thomaz, A. L., & Breazeal, C. (2008). Teachable robots: Understanding human teaching behavior to build more effective robot learners. Artificial Intelligence, 172, 716–737.CrossRefGoogle Scholar
  10. Wooldridge, M. (2000). Reasoning about rational agents. The MIT.Google Scholar
  11. Xu, Y., Ohmoto, Y., Okada, S., Ueda, K., Komatsu, T., Okadome, T., et al. (2010). Formation conditions of mutual adaptation in human-agent collaborative interaction. Applied Intelligence. doi:10.1007/s10489-010-0255-y.
  12. Xu, Y., Ohmoto, Y., Ueda, K., Komatsu, T., Okadome, T., Kamei, K., et al. (2008). Two-layered communicative protocol model in a cooperative directional guidance task. In The 7th international workshop on social intelligence design. Puerto Rico.Google Scholar
  13. Xu, Y., Ohmoto, Y., Ueda, K., Komatsu, T., Okadome, T., Kamei, K., et al. (2009a). A platform system for developing a collaborative mutually adaptive agent. In International conference on industrial, engineering & other applications of applied intelligent systems (IEA/AIE 2009). Lecture notes in computer science: Next-generation applied intelligence (Vol. 5579, pp. 576–585). Berlin: Springer.Google Scholar
  14. Xu, Y., Ueda, K., Komatsu, T., Okadome, T., Hattori, T., Sumi, Y., et al. (2009b). Woz experiments for understanding mutual adaptation. Journal of AI & Society, 23(2), 201–212.CrossRefGoogle Scholar
  15. Yamada, S., & Kakusho, K. (2003). IDEA: Interaction design for adaptation (in Japanese). Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 15(2), 185.Google Scholar
  16. Yamada, S., & Yamaguchi, T. (2004). Training AIBO like a dog. In The 13th international workshop on robot and human interactive communication (ROMAN-2004) (pp. 431–436). Kurashiki, Japan.Google Scholar
  17. Yamada, S., & Yamaguchi, T. (2005). Mutual adaptation of mind mappings between a human and a life-like agent. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 17(3), 289–297.Google Scholar
  18. Yamasaki, N., & Anzai, Y. (1995). Active interface for human-robot interaction. In IEEE International Conference on Robotics and Automation, 3, 3103–3109.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Yong Xu
    • 1
  • Yoshimasa Ohmoto
    • 2
  • Kazuhiro Ueda
    • 3
  • Takanori Komatsu
    • 4
  • Takeshi Okadome
    • 5
  • Koji Kamei
    • 6
  • Shogo Okada
    • 2
  • Yasuyuki Sumi
    • 2
  • Toyoaki Nishida
    • 2
  1. 1.Division of Advanced Information Technology & Computer Science, Institute of EngineeringTokyo University of Agriculture and TechnologyKoganei-shiJapan
  2. 2.Graduate School of Informatics, Kyoto UniversitySakyo-kuJapan
  3. 3.Department of System SciencesThe University of TokyoTokyoJapan
  4. 4.International Young Researcher Empowerment CenterShinshu UniversityNaganoJapan
  5. 5.School of Technology and ScienceKwansei Gakuin UniversitySandaJapan
  6. 6.Advanced Telecommunications Research Institute InternationalSoraku-gunJapan

Personalised recommendations