BiPOCL: A Discourse-Driven Story Planner for Procedural Narrative Generation (Extended Abstract)

Narrative intelligence is fundamental for organizing experiences, understanding our surroundings, and forming predictions about the future [Schank, 1995]. Computational systems that leverage narrative intelligence benefit applications that involve stories including entertainment, education, skill training, and legal decision-making. AI planning research is a popular source of data structures and algorithms for understanding, generating, and reasoning about stories [Young et al., 2014]. Narratologists frequently distinguish the story (i.e. fabula) of narrative from the discourse [Chatman, 1980], and plans have proven useful for modeling both story and discourse [Young, 2007]; they are effective for modeling discourse because a coherent sequence of communicative actions is plan-like [Cohen and Perrault, 1979], and plans are effective for modeling stories because stories are composed of events with cause-effect relations with characters themselves forming plans to achieve goals. Psychological studies have demonstrated that plans capture many of the key aspects of narratives that spectators use to understand narrative discourse [Radvansky et al., 2014; Cardona-Rivera et al., 2016].