PNFG : a framework for computer game narrative analysis
Bibliographic record
Abstract
Narratives play a significant role in many computer games, and this is especially true in genres such as role-playing and adventure games. Even so, many games have narratives which possess a certain number of flaws that can deteriorate the playing experience. This less than satisfying gameplay experience can obviously affect the commercial success of a given game. Our research originates from the need to identify these narrative flaws. In response to this need, we present a framework for computer game narratives analysis. Our work focuses on Interactive Fiction games, which are textual, command-line and turn-based games. We first describe a high level computer narrative language, the Programmable Narrative Flow Graph (PNFG), that provides a high level, user-friendly interface to a low level formalism, the Narrative Flow Graph (NFG) [38]. The PNFG language is delivered with a set of enhancements and low level optimizations that reduce the size of the generated NFG output. As part of our work on the analysis of narrative structures, we developed a proof of concept heuristic solver that attempts to automatically find solutions to games from a lightweight high
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".