Advertisement

An Integrated Approach for Generating Arguments and Rebuttals and Understanding Rejoinders

  • Ingrid Zukerman
Conference paper
  • 688 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)

Abstract

This paper describes an integrated approach for interpreting a user’s responses and generating replies in the framework of a WWW-based Bayesian argumentation system. Our system consults a user model which represents a user’s beliefs, inferences and attentional focus, as well as the system’s certainty regarding the user’s beliefs. The interpretation mechanism takes into account these factors to infer the intended effect of the user’s response on the system’s argument. The reply-generation mechanism focuses on the identification of discrepancies between the beliefs in the user model and the beliefs held by the system that are relevant to the inferred interpretation.

Keywords

argumentation Bayesian networks plan recognition discourse planning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.R. Anderson. The Architecture of Cognition. Harvard University Press, Cambridge, Massachusetts, 1983.Google Scholar
  2. 2.
    Sandra Carberry and Lynn Lambert. A process model for recognizing communicative acts and modeling negotiation subdialogues. Computational Linguistics, 25(1):1–53, 1999.Google Scholar
  3. 3.
    Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):50–56, 1993.CrossRefGoogle Scholar
  4. 4.
    Abigail Gertner, Cristina Conati, and Kurt VanLehn. Procedural help in Andes: Generating hints using a Bayesian network student model. In AAAI98-Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 106–111, Madison, Wisconsin, 1998.Google Scholar
  5. 5.
    David Heckerman and Eric Horvitz. Inferring informational goals from free-text queries: A Bayesian approach. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 230–237, Madison, Wisconsin, 1998.Google Scholar
  6. 6.
    Nathalie Jitnah, Ingrid Zukerman, Richard McConachy, and Sarah George. Towards the generation of rebuttals in a Bayesian argumentation system. In Proceedings of the First International Natural Language Generation Conference, pages 39–46, Mitzpe Ramon, Israel, 2000.Google Scholar
  7. 7.
    Alex Quilici. Arguing about planning alternatives. In COLING-92-Proceedings of the Fourteenth International Conference on Computational Linguistics, pages 906–910, Nantes, France, 1992.Google Scholar
  8. 8.
    Ingrid Zukerman, Nathalie Jitnah, Richard McConachy, and Sarah George. Recognizing intentions from rejoinders in a Bayesian interactive argumentation system. In PRICAI2000-Proceedings of the Sixth Pacific Rim International Conference on Artificial Intelligence, pages 252–263, Melbourne, Australia, 2000.Google Scholar
  9. 9.
    Ingrid Zukerman, Richard McConachy, and Kevin B. Korb. Bayesian reasoning in an abductive mechanism for argument generation and analysis. In AAAI98-Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 833–838, Madison,Wisconsin, 1998.Google Scholar
  10. 10.
    Ingrid Zukerman, Richard McConachy, Kevin B. Korb, and Deborah A. Pickett. Exploratory interaction with a Bayesian argumentation system. In IJCAI99-Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1294–1299, Stockholm, Sweden, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ingrid Zukerman
    • 1
  1. 1.School of Computer Science and Software EngineeringMonash UniversityClaytonAustralia

Personalised recommendations