Abstract
We describe a probabilistic approach for the interpretation of user arguments, and investigate the incorporation of different models of a user’s beliefs and inferences into this mechanism. Our approach is based on the tenet that the interpretation intended by the user is that with the highest posterior probability. This approach is implemented in a computer-based detective game, where the user explores a virtual scenario, and constructs an argument for a suspect’s guilt or innocence. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation – a Bayesian network. This interpretation may differ from the user’s argument in its structure and in its beliefs in the argument propositions. We conducted a synthetic evaluation of the basic interpretation mechanism, and a user-based evaluation which assesses the impact of the different user models. The results of both evaluations were encouraging, with the system generally producing argument interpretations our users found acceptable.
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Zukerman, I., George, S. A Probabilistic Approach for Argument Interpretation. User Model User-Adap Inter 15, 5–53 (2005). https://doi.org/10.1007/s11257-004-5660-7
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DOI: https://doi.org/10.1007/s11257-004-5660-7