Plot (narrative)

Abstract: Preface 1. Reading for the Plot 2. Narrative Desire 3. The Novel and the Guillotine, or Fathers and Sons in Le Rouge et le noir 4. Freud's Masterplot: A Model for Narrative 5. Repetition, Repression, and Return: The Plotting of Great Expectations 6. The Mark of the Beast: Prostitution, Serialization, and Narrative 7. Retrospective Lust, or Flaubert's Perversities 8. Narrative Transaction and Transference 9. An Unreadable Report: Conrad's Heart of Darkness 10. Fictions of the Wolf Man: Freud and Narrative Understanding 11. Incredulous Narration: Absalom, Absalom! In Conclusion: Endgames and the Study of Plot Notes

Abstract: Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors - logical and aesthetic - that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem - to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm - the Intent-based Partial Order Causal Link (IPOCL) planner - that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.

Abstract: Reexamines the development and the popular conception of narrative literature and looks at meaning, characterization, plot, and point of view in various narrative forms.