Generation and selection of likely interpretations during plan recognition in task-oriented consultation systems

Bhavani Raskutti, Ingrid Zukerman

    Research output: Contribution to journalArticleResearchpeer-review

    18 Citations (Scopus)


    This paper presents a mechanism which infers a user's plans from his/her utterances by directing the inference process towards the more likely interpretations of a speaker's statements among many possible interpretations. Our mechanism uses Bayesian theory of probability to assess the likelihood of an interpretation, and it complements this assessment by taking into consideration two aspects of an interpretation: its coherence and its information content. The coherence of an interpretation is determined by the relationships between the different statements in the discourse. The information content of an interpretation is a measure of how well defined the interpretation is in terms of the actions to be performed on the basis of this interpretation. This measure is used to guide the inference process towards interpretations with higher information content. The information content of an interpretation depends on the specificity and the certainty of the inferences in it, where the certainty of an inference depends on the knowledge on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of travel booking.

    Original languageEnglish
    Pages (from-to)323-353
    Number of pages31
    JournalUser Modeling and User-Adapted Interaction
    Issue number4
    Publication statusPublished - 1 Dec 1991


    • Bayesian Theory
    • Cooperative Domains
    • Inference Classification
    • Intelligent Interfaces
    • Multiple Interpretations
    • Plan Inference
    • Plan Recognition in NLI
    • Plausible Interpretations

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