There are two disciplines that are concerned with the same object of study, narratives, but that rarely exchange insights and ideas, let alone engage in collaborative research. The first is Narrative Theory (NT), an analytical discipline from the humanities that attempts to analyze literary texts and from these instances derive a general understanding of the concept of narrative. The second is Computational Story Composition (CSC), a discipline in the domain of Artificial Intelligence that attempts to enable computers to autonomously compose fictional narratives in a way that could be deemed creative. Several reasons can be found for the lack of collaboration, but one of them stands out: The two disciplines follow decidedly different research methodologies at contradistinct levels of abstraction. This makes it hard to conduct NT and CSC research simultaneously, and also means that CSC researchers have a hard time validating whether they use NT concepts correctly, while NT scholars have no use for the outputs created by work in CSC. At the same time, a close exchange between the two disciplines would be desirable, not only because of the complementary approach to their object of study, but also because comparable interdisciplinary collaborations have proven to be productive in other fields, like for instance linguistics.
The present thesis proposes a research methodology called generative modeling designed to address the methodological differences outlined above, and thus allow to conduct simultaneous NT and CSC research.As a proof of concept it performs several cycles of generative modeling, in which it computationally implements concepts and dynamics described in two frameworks from NT, namely Marie-Laure Ryan’s possible worlds approach to plot, and Alan Palmer’s fictional minds approach to characters. In detail, the first cycle attempts to implement Ryan’s possible worlds semantics and the resulting dynamics of plot, but falls short in a way that suggests that the first principles laid out in the theory are not sufficient to capture an example plot, for a number of reasons. The second cycle resolves these hypothesized problems by extending Ryan’s plot understanding with affective dynamics based on Palmer’s understanding of fictional minds. With plot dynamics completed, the third cycle implements Ryan’s concept of tellability, which represents a quantifiable measure of the structural quality of plots. The last cycle implements a Genetic Algorithm based search heuristic that is capable of searching the plot space spanned by the employed formalism for plots high in tellability, which provides additional insights on properties of tellability. The resulting implementation is an in-depth computational representation of plot ingrained into the CSC System InBloom, which is capable of autonomously composing novel plots and evaluating their quality.
The study reported in this thesis demonstrates, how implementing narratological theories as generative models can lead to insights for NT, and how grounding computational representations of narrative in NT can help CSC systems take over creative responsibilities. Thereby, it shows the feasibility and utility of generative modeling.