Abductive Inference of Plans and Intentions in Information-Seeking Dialogues

  • Paulo Quaresma
  • José Gabriel Lopes


A robust man-machine interaction requires the capability for inferring the beliefs and intentions of each active agent. In this article it will be proposed a framework that supports the recognition of plans and intentions through abductive inferences over discourse sentences. The possible actions, world knowledge, events and states are represented by extended logic programs (LP with explicit negation) and the abductive inference process is modeled by the framework proposed by Pereira ([PAA92]) which is based on the Well Founded Semantics augmented with explicit negation (WFSX) and contradiction removal semantics (CRSX). It will be shown how this framework supports abductive planning with Event Calculus ([Esh88]) and some classical examples will be shown ([Lit 85, Pol 86]) in the domain of information-seeking dialogues. Finally, some open problems and future work will be pointed out.


Logic Program Abductive Reasoning User Goal Abductive Inference Hypothetical Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [AP92]
    Douglas E. Appelt and Martha E. Pollack. Weighted abduction for plan ascription. User Modeling and User-Adapted Interaction, 2(1), 1992.Google Scholar
  2. [CL90]
    P. Cohen and H. Levesque. Intention is choice with commitment. Artificial Intelligence, 42(3), 1990.Google Scholar
  3. [CMP90]
    P. Cohen, J. Morgan, and M. Pollack. Intentions in Communication. MIT Press, Cambridge, MA, 1990.Google Scholar
  4. [Esh88]
    Kave Eshghi. Abductive planning with event calculus. In ICLP, 1988.Google Scholar
  5. [FN71]
    R. E. Fikes and Nils J. Nilsson. Strips: A new approach to the application of theorem proving to problem solving. Artificial Intellligence, (2):189–208, 1971.zbMATHCrossRefGoogle Scholar
  6. [LA87]
    D. Litman and J. Allen. Aplan recognition model for subdialogues in conversations. Cognitive Science, (11):163–200, 1987.CrossRefGoogle Scholar
  7. [Lit85]
    Diane J. Litman. Plan Recognition and Discourse Analysis: An Integrated Approach for Understanding Dialogues. PhD thesis, Dep. of Computer Science, University of Rochester, 1985.Google Scholar
  8. [Lop91]
    J. G. Lopes. Architecture for intentional participation of natural language interfaces in conversations. In C. Brown and G. Koch, editors, Natural Language Understanding and Logic Programming III. North Holland, 1991.Google Scholar
  9. [Mis91]
    Lode Missiaen. Localized Abductive Planning with the Event Calculus. PhD thesis, Univ. Leuven, 1991.Google Scholar
  10. [PAA92]
    L. M. Pereira, J. J. Alferes, and J. N. Aparício. Contradiction removal semantics with explicit negation. In Proc. Applied Logic Conf., 1992.Google Scholar
  11. [Pol 86]
    Martha E. Pollack. Inferring Domain Plans in Question-Answering. PhD thesis, Dep. of Computer and Information Science, University of Pennsylvania, 1986.Google Scholar
  12. [QL92]
    P. Quaresma and J. G. Lopes. A two-headed architecture for intelligent multimedia man-machine interaction. In Artificial Intelligence V-methodology, systems, applications. B. de Boulay and V. Sgurev, 1992.Google Scholar
  13. [Sac77]
    Earl D. Sacerdoti. A Structure for Plans and Behavior. American Elsevier, New York, 1977.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Paulo Quaresma
    • 1
  • José Gabriel Lopes
    • 1
  1. 1.Artificial Intelligence CenterUNINOVAMonte da CaparicaPortugal

Personalised recommendations