Opponent modelling in persuasion dialogues

Abstract

A strategy is used by a participant in a persuasion dialogue to select locutions most likely to achieve its objectives of persuading its opponent. Such strategies often assume that the participant has a model of its opponents, which may be constructed on the basis of a participant's accumulated dialogue experience. However, in most cases the fact that an agent's experience may encode additional information which if appropriately used could increase a strategy's efficiency, is neglected. In this work, we rely on an agent's experience to define a mechanism for augmenting an opponent model with information likely to be dialectally related to information already contained in it. Precise computation of this likelihood is exponential in the volume of related information. We thus describe and evaluate an approximate approach for computing these likelihoods based on Monte-Carlo simulation.