JRM Vol.17 No.6 pp. 697-704
doi: 10.20965/jrm.2005.p0697


A Dialogue Control Model Based on Ambiguity Evaluation of Users’ Instructions and Stochastic Representation of Experiences

Tetsunari Inamura, Masayuki Inaba, and Hirochika Inoue

Dept. of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

February 3, 2005
July 11, 2005
December 20, 2005
human-robot interaction, Bayesian networks, dialogue planning, user adaptation, experience based behavior

To operate in everyday environments, robots much accomplish complex tasks following often mbiguous and uncertain instructions, mainly through advanced inference or recognition. We focus on an intelligent human-robot interaction framework that reduces the burden of the user. Robots compensate for ambiguities by active sensing and dialogue control through questions and suggestions to users. Robots also use experience to reduce the user’s burden. We propose a criterion for ambiguity evaluation of user instructions, stochastic representation of personal experiences and a dialogue control model for accomplishing tasks in complex environments. We demonstrate the feasibility of our proposal in demonstrate experiment where a robot searches for an object in disorganized work space with ambiguous instructions.

Cite this article as:
Tetsunari Inamura, Masayuki Inaba, and Hirochika Inoue, “A Dialogue Control Model Based on Ambiguity Evaluation of Users’ Instructions and Stochastic Representation of Experiences,” J. Robot. Mechatron., Vol.17, No.6, pp. 697-704, 2005.
Data files:
  1. [1] T. Inamura, M. Inaba, and H. Inoue, “Pexis: Probabilistic experience representation based adaptive interaction system for personal robots,” Systems and Computers in Japan, 35(6): pp. 98-109, 2004.
  2. [2] M. L. Dertouzos, “The future of computing,” Scientific American, 1999.
  3. [3] A. Pentland, “Smart rooms,” Scientific American, pp. 54-62, 1996.
  4. [4] R. Brooks, “The intelligent room project,” In Proceedings of the 2nd International Cognitive Technology Conference (CT’97), 1997.
  5. [5] T. Sato, S. Otani, S. Itoh, T. Harada, and T. Mori, “Human behavior logging support system utilizing fused pose/position sensor and behavior target sensor information,” In Proc. of International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2003.
  6. [6] J. Pearl, “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference,” Morgan Kaufmann, 1988.
  7. [7] K. Basye, T. Dean, J. Kirman, and M. Lejter, “A Decision-Theoretic Approach to Planning Perception and Control,” IEEE Expert, 7(4): pp. 58-65, 1992.
  8. [8] H. Asoh, Y. Motomura, I. Hara, S. Akaho, S. Hayamizu, and T. Matsui, “Acquiring a Probabilistic Map with Dialogue-Based Learning,” In Proceedings of ROBOLEARN-96, 1996.
  9. [9] D. Heckerman, A. Mamdani, and M. P. Wellman, “Real-World Applications of Bayesian Networks,” Communicaiton of the ACM, 38(3): pp. 24-57, March, 1995.
  10. [10] T. Akiba, and H. Tanaka, “A Bayesian Approach for User Modeling in Dialogue Systems,” Technical report, Dept. of Computer Science, Tokyo Institute of Technology, 1994.
  11. [11] T. Inamura, M. Inaba, and H. Inoue, “Integration model of learning mechanism and dialogue strategy based on stochastic experience representation using bayesian network,” In The International Workshop on Robot and Human Interactive Communication (ROMAN 2000), pp. 247-252, 2000.
  12. [12] T. Inamura, M. Inaba, and H. Inoue, “Acquisition of Probabilistic Behavior Decision Model based on the Interactive Teaching Method,” In Proceeding of the 9th International Conference on Advanced Robotics, pp. 523-528, 1999.
  13. [13] T. Inamura, K. Naka, M. Inaba, and H. Inoue, “Human-centered adaptive mobile robot based on on-line dialogue and stochastic experience representation,” In Preprints of the Fourth IFAC Symposium on Intelligent Autonomous Vehicle (IAV2001), pp. 61-66, 2001.
  14. [14] T. Inamura, M. Inaba, and H. Inoue, “User adaptation of human-robot interaction model based on bayesian network and intro-spection of interaction experience,” In Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS 2000), pp. 2139-2144, 2000.

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