Deciding what not to say: An attentional-probabilistic approach to argument presentation

Effective arguments must be presented in a cohesive manner: simple collections of believed premises and connecting inferences supporting a goal may not persuade the recipient if they are not well ordered. We use semantic activation and Bayesian propagation in a user model to simulate the effect of presenting an argument generated by our system, NAG, to the user. This simulation is used to select a strategy for presenting the argument to the user. The simulation also identifies superfluous lines of reasoning that may be removed, and enables NAG to determine how multiple subarguments for points should be presented, e.g., as multiple individual supports or collectively. A greedy algorithm is then used to apply probabilistic pruning and semantic suppression to further simplify the argument. Probabilistic pruning removes unnecessary premises from the argument. Semantic suppression is used to select portions of the argument which are within the user’s focus of attention, and which are also readily inferred, and hence can be left implicit without damaging the effectiveness of the argument