Abstract
Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used computation time need to be balanced. This becomes challenging, if the number of sensors is high and measurement systems are complex. In this paper, a model predictive approach to this problem based on online switching of small, situation-specific Dynamic Bayesian Networks is proposed. The contributions are an efficient modeling and inference of situations and a greedy model predictive switching algorithm maximizing the mutual information of predicted situations. The achievable accuracy and computational savings are demonstrated for a household scenario by using an extended range telepresence system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schmidt, C.F., Sridharan, N.S., Goodson, J.L.: The Plan Recognition Problem: An Intersection of Psychology and Artificial Intelligence. Artificial Intelligence 11(1-2), 45–83 (1978)
Schrempf, O.C., Hanebeck, U.D., Schmid., A.J., Wörn, H.: A Novel Approach to Proactive Human-Robot Cooperation. In: Proceedings of the 2005 IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), Nashville, Tennessee, pp. 555–560 (2005)
Kautz, H.A.: A Formal Theory of Plan Recognition and its Implementation. In: Reasoning About Plans, pp. 69–125. Morgan Kaufmann Publishers, San Mateo (1991)
Pynadath, D.V., Wellman, M.P.: Probabilistic State-Dependent Grammars for Plan Recognition. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI 2000), pp. 507–514. Morgan Kaufmann Publishers, Inc., San Francisco (2000)
Bui, H.H.: A General Model for Online Probabilistic Plan Recognition. In: Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1309–1315 (2003)
Geib, C.W., Goldman, R.P.: Partial Observability and Probabilistic Plan/Goal Recognition- ijcai 2005 workshop on modeling others from observations (2005)
Murphy, K.: Dynamic Bayesian Network: Representation, Inference and Learning. PhD thesis, UC Berkeley (2002)
Tahboub, K.A.: Intelligent Human-Machine Interaction Based on Dynamic Bayesian Networks Probabilistic Intention Recognition. Journal of Intelligent and Robotic Systems 45(1), 31–52 (2006)
Krauthausen, P., Hanebeck, U.D.: Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks. In: Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington (2009)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann Publishers, Inc, San Francisco (1988)
Koller, D.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Koller, D., Pfeffer, A.: Object-Oriented Bayesian Networks. In: Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI 1997), Providence, Rhode Island, pp. 302–313 (1997)
Laskey, K., Mahoney, S.: Network Fragments: Representing Knowledge for Constructing Probabilistic Models. In: Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI 1997), Providence, Rhode Island, pp. 334–341 (1997)
Bilmes, J.: Dynamic Bayesian Multinets. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 38–45. Morgan Kaufmann Publishers, Inc., San Francisco (2000)
Williams, J.L.: Information Theoretic Sensor Management. PhD thesis, Massachusetts Institute of Technology (2007)
Zhang, Y., Ji, Q.: Efficient Sensor Selection for Active Information Fusion. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 99 (2009), ISSN: 1083–4419
Schrempf, O., Hanebeck, U.D.: Evaluation of Hybrid Bayesian Networks using Analytical Density Representations. In: Proc. of the 16th IFAC World Congress (IFAC 2005), Czech Republic (2005)
Rößler, P., Beutler, F., Hanebeck, U.D., Nitzsche, N.: Motion Compression Applied to Guidance of a Mobile Teleoperator. In: Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pp. 2495–2500 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krauthausen, P., Hanebeck, U.D. (2010). Situation-Specific Intention Recognition for Human-Robot Cooperation. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-16111-7_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16110-0
Online ISBN: 978-3-642-16111-7
eBook Packages: Computer ScienceComputer Science (R0)