[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Generating Artificial Corpora for Plan Recognition

  • Conference paper
User Modeling 2005 (UM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3538))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agre, P., Horswill, I.: Cultural support for improvisation. In: Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI 1992 (1992)

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7 (2003)

    Google Scholar 

  4. Bauer, M.: Quantitative modeling of user preferences for plan recognition. In: UM, Hyannis, Massachusetts (1994)

    Google Scholar 

  5. Bauer, M.: Acquisition of user preferences for plan recognition. In: Proceedings of the Fifth International Conference on User Modeling, Kailua-Kona, Hawaii (1996)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. Bui, H.H., Venkatesh, S., West, G.: Policy recognition in the Abstract Hidden Markov Model. Journal of Artificial Intelligence Research 17, 451–499 (2002)

    MATH  MathSciNet  Google Scholar 

  8. Blaylock, N., Allen, J.: Corpus-based, statistical goal recognition. In: IJCAI, Acapulco, Mexico (2003)

    Google Scholar 

  9. Blaylock, N., Allen, J.: Statistical goal parameter recognition. In: ICAPS, Whistler, British Columbia (2004)

    Google Scholar 

  10. Charniak, E., Goldman, R.P.: A Bayesian model of plan recognition. Artificial Intelligence 64, 53–79 (1993)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. Kellner, A.: Initial language models for spoken dialogue systems. In: Proceedings of ICASSP 1998, Seattle, Washington (1998)

    Google Scholar 

  14. Lesh, N.: Scalable and Adaptive Goal Recognition. PhD thesis, University of Washington (1998)

    Google Scholar 

  15. 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)

    MATH  Google Scholar 

  16. 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)

    Chapter  Google Scholar 

  17. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics