Generating QUEST Representations for Narrative Plans Consisting of Failed Actions
Rushit Sanghrajka, Eric W. Lang, R. Michael Young · 2021
Increasingly, research on narrative planning is expanding the expressive range of narrative generation systems, producing plot lines with structures like failed action, mistaken character belief and the integration of authorial and character-centered plans and intentions. Evaluation of these systems’ expressive capabilities is essential to determining their strengths. Some prior evaluative methods have measured the efficacy of narrative planners by characterizing a user’s experience during generated narratives. These approaches have focused on comparing the mental model a user forms during the experience of a narrative with the plan data structure that served as the basis of the narrative’s plot, but have not considered evaluating the user’s understanding of failed actions in narrative. To that end, we propose an algorithm to translate plans containing failed actions into a commonly used cognitive mode of narrative comprehension called QUEST. We then sketch how this translation will play a role in a planned evaluation of users’ experiences reading stories produced by narrative planning systems that generate stories with failed actions.