Abstract
Corpora for training plan recognizers are scarce and difficult to gather from humans. However, corpora could be a boon to plan recognition research, providing a platform to train and test individual recognizers, as well as allow different recognizers to be compared. We present a novel method for generating artificial corpora for plan recognition. The method uses a modified AI planner and Monte-Carlo sampling to generate action sequences labeled with their goal and plan. This general method can be ported to allow the automatic generation of corpora for different domains.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agre, P., Horswill, I.: Cultural support for improvisation. In: Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI 1992 (1992)
Albrecht, D.W., Zukerman, I., Nicholson, A.E.: Bayesian models for keyhole plan recognition in an adventure game. User Modeling and User-Adapted Interaction 8, 5–47 (1998)
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7 (2003)
Bauer, M.: Quantitative modeling of user preferences for plan recognition. In: UM, Hyannis, Massachusetts (1994)
Bauer, M.: Acquisition of user preferences for plan recognition. In: Proceedings of the Fifth International Conference on User Modeling, Kailua-Kona, Hawaii (1996)
Bauer, M.: Acquisition of abstract plan descriptions for plan recognition. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998), Madison, WI, pp. 936–941 (1998)
Bui, H.H., Venkatesh, S., West, G.: Policy recognition in the Abstract Hidden Markov Model. Journal of Artificial Intelligence Research 17, 451–499 (2002)
Blaylock, N., Allen, J.: Corpus-based, statistical goal recognition. In: IJCAI, Acapulco, Mexico (2003)
Blaylock, N., Allen, J.: Statistical goal parameter recognition. In: ICAPS, Whistler, British Columbia (2004)
Charniak, E., Goldman, R.P.: A Bayesian model of plan recognition. Artificial Intelligence 64, 53–79 (1993)
Davison, B.D., Hirsh, H.: Predicting sequences of user actions. In: Notes of the AAAI/ICML 1998 Workshop on Predicting the Future: AI Approaches to Time-Series Analysis, Madison, Wisconsin (1998)
Huber, M.J., Durfee, E.H., Wellman, M.P.: The automated mapping of plans for plan recognition. In: de Mantaras, R.L., Poole, D. (eds.) UAI 1994 - Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, pp. 344–351. Morgan Kaufmann, San Francisco (1994)
Kellner, A.: Initial language models for spoken dialogue systems. In: Proceedings of ICASSP 1998, Seattle, Washington (1998)
Lesh, N.: Scalable and Adaptive Goal Recognition. PhD thesis, University of Washington (1998)
Nau, D., Au, T.C., Ilghami, O., Kuter, U., Murdock, J.W., Wu, D., Yaman, F.: SHOP2: An HTN planning system. Journal of Artificial Intelligence Research 20, 379–404 (2003)
Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)
Pynadath, D.V., Wellman, M.P.: Accounting for context in plan recognition, with application to traffic monitoring. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Canada. Morgan Kaufmann, San Francisco (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Blaylock, N., Allen, J. (2005). Generating Artificial Corpora for Plan Recognition. In: Ardissono, L., Brna, P., Mitrovic, A. (eds) User Modeling 2005. UM 2005. Lecture Notes in Computer Science(), vol 3538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527886_24
Download citation
DOI: https://doi.org/10.1007/11527886_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27885-6
Online ISBN: 978-3-540-31878-1
eBook Packages: Computer ScienceComputer Science (R0)