A Bayesian Approach to Reasoning under Uncertainty in Natural Language Dialogues
S. Keizer (Keynote speaker)
Activity: Talk or presentation › Oral presentation
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.
3 Nov 2000
11th Meeting on Computational Linguistics in the Netherlands, CLIN 2000