A Bayesian Approach to Reasoning under Uncertainty in Natural Language Dialogues

S. Keizer (Keynote speaker)

    Activity: Talk or presentationOral presentation

    Description

    In the talk I would like to present a probabilistic approach to
    dealing with various kinds of uncertainty in modelling natural
    language dialogues. The uncertainties a conversational agent has to
    deal with arise in:
    1. the inaccuracy and incompleteness of the linguistic information
    extracted from a user utterance by a parser,
    2. the interpretation of this information in terms of communicative
    actions or dialogue acts,
    3. updating the agent's mental model of the user on which the user's
    behaviour is based, and
    4. updating the agent's mental model of the user when the agent itself
    has performed a certain action.

    I will give a sketch of how Dynamic Decision Networks (an extension
    of Bayesian Networks) could be used for modelling a conversational
    agent which can deal with incomplete information, make 'educated
    guesses' concerning interpretation, and 'do the right thing', based on
    Bayesian updating and utility-directed action. I will indicate which
    problems arise when using this approach and what work needs to be done
    in order to solve these problems.
    Period3 Nov 2000
    Event title11th Meeting on Computational Linguistics in the Netherlands, CLIN 2000
    Event typeConference
    Conference number11
    LocationTilburg, Netherlands
    Degree of RecognitionInternational