A GENERIC MODEL FOR ESTIMATING USER INTENTIONS IN HUMAN-ROBOT COOPERATION

Oliver C. Schrempf, Uwe D. Hanebeck

Abstract

The recognition of user intentions is an important feature for humanoid robots to make implicit and human-like interactions possible. In this paper, we introduce a formal view on user-intentions in human-machine interaction and how they can be estimated by observing user actions. We use Hybrid Dynamic Bayesian Networks to develop a generic model that includes connections between intentions, actions, and sensor measurements. This model can be used to extend arbitrary human-machine applications by intention recognition.

References

  1. Breazeal, C. (1999). Robots in Society: Friend or Appliance? In Agents99 Workshop on Emotion-based Agent Architecture, pages 18-26, Seattle, WA.
  2. Driver, E. and Morrell, D. (1995). Implementation of Continuous Bayesian Networks Using Sums of Weighted Gaussians. In Besnard and Hanks, editors, Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pages 134-140, Montreal, Quebec, Canada.
  3. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., and Rommelse, K. (1998). The Lumière Project: Bayesian User Modelling for Inferring the Goals and Needs of Software Users. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 256-265. AUAI, Morgan Kaufman.
  4. Korb, K. and Nicholson, A. E. (2003). Bayesian Artificial Intelligence. Chapman & Hall/CRC.
  5. Lauritzen, S. L. (1992). Propagation of Probabilities, Means and Variances in Mixed Graphical Association Models. Journal of the American Statistical Association, 87(420):1098-1108.
  6. Murphy, K. P. (2002). Dynamic Bayesian Networks. http://www.ai.mit.edu/~murphyk. To appear in Probabilistic Graphical Models (M. Jordan ed.).
  7. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
  8. Poland, W. and Shachter, R. (1993). Mixtures of Gaussians and Minimum Relative Entropy Techniques for Modeling Continuous Uncertainties. In Proceedings of the 9th Annual Conference on Uncertainty in Artificial Intelligence (UAI-93), San Francisco, CA. Morgan Kaufmann Publishers.
  9. Rabiner, L. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In Proceedings of the IEEE, volume 77, pages 257-286.
  10. Roweis, S. and Ghahramani, Z. (1999). A Unifying Review of Linear Gaussian Models. Neural Computing, 11:305-345.
  11. Schrempf, O. C. and Hanebeck, U. D. (2004). A New Approach for Hybrid Bayesian Networks Using Full Densities. In Proceedings of 6th Workshop on Computer Science and Information Technologies, CSIT 2004, Budapest, Hungary.
  12. Schrempf, O. C. and Hanebeck, U. D. (2005). Evaluation of Hybrid Bayesian Networks using Analytical Density Representations. In Proceedings of the 16th IFAC World Congress, IFAC 2005, Prague, Czech Republic.
Download


Paper Citation


in Harvard Style

C. Schrempf O. and D. Hanebeck U. (2005). A GENERIC MODEL FOR ESTIMATING USER INTENTIONS IN HUMAN-ROBOT COOPERATION . In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 4: ICINCO, ISBN 972-8865-30-9, pages 251-256. DOI: 10.5220/0001166002510256


in Bibtex Style

@conference{icinco05,
author={Oliver C. Schrempf and Uwe D. Hanebeck},
title={A GENERIC MODEL FOR ESTIMATING USER INTENTIONS IN HUMAN-ROBOT COOPERATION},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 4: ICINCO,},
year={2005},
pages={251-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001166002510256},
isbn={972-8865-30-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 4: ICINCO,
TI - A GENERIC MODEL FOR ESTIMATING USER INTENTIONS IN HUMAN-ROBOT COOPERATION
SN - 972-8865-30-9
AU - C. Schrempf O.
AU - D. Hanebeck U.
PY - 2005
SP - 251
EP - 256
DO - 10.5220/0001166002510256