Generation of Potential Narratives in Interactive Fiction
Bibliographic record
Abstract
Digital media has created more opportunities for stories to be interactive, allowing the user to participate in plot events and even change the direction of a story. Interactivity can make stories more engaging however if the plot can change as a result of user input then the story becomes non-linear. The resulting system is known as a potential narrative because a narrative is only presented through user interaction. Non-linear stories require extra considerations and content compared to a linear story of similar length. Computer generated content for video games has grown as a field and this raises the possibility of using computer assistance for the creation and management of interactive stories. This thesis explores the paradigm required for both computers and developers to understand potential narratives. The success of a potential narrative requires the same coherency and credibility that a regular story does, in addition to a new layer known as player agency. Coherency refers to the logical causal progression of a plot, credibility is the verisimilitude of the presented story world and player agency is how satisfying the interactive elements are. These three criteria are the guiding principles for the creation of an intuitive potential narrative model. We present a state transition model for potential narratives as well as a prototype using relational programming to generate coherent traversals through a manually authored potential narrative.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".