Tension Space Analysis for Emergent Narrative
- URL: http://arxiv.org/abs/2004.10808v1
- Date: Wed, 22 Apr 2020 19:26:09 GMT
- Title: Tension Space Analysis for Emergent Narrative
- Authors: Ben Kybartas, Clark Verbrugge, Jonathan Lessard
- Abstract summary: We present a novel approach to emergent narrative using the narratological theory of possible worlds.
We demonstrate how the design of works in such a system can be understood through a formal means of analysis inspired by expressive range analysis.
Finally, we propose a novel way through which content may be authored for the emergent narrative system using a sketch-based interface.
- Score: 0.1784936803975635
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Emergent narratives provide a unique and compelling approach to interactive
storytelling through simulation, and have applications in games, narrative
generation, and virtual agents. However the inherent complexity of simulation
makes understanding the expressive potential of emergent narratives difficult,
particularly at the design phase of development. In this paper, we present a
novel approach to emergent narrative using the narratological theory of
possible worlds and demonstrate how the design of works in such a system can be
understood through a formal means of analysis inspired by expressive range
analysis. Lastly, we propose a novel way through which content may be authored
for the emergent narrative system using a sketch-based interface.
Related papers
- Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents.
We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
arXiv Detail & Related papers (2024-10-03T15:44:42Z) - Are Large Language Models Capable of Generating Human-Level Narratives? [114.34140090869175]
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression.
We introduce a novel computational framework to analyze narratives through three discourse-level aspects.
We show that explicit integration of discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling.
arXiv Detail & Related papers (2024-07-18T08:02:49Z) - StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning [8.851718319632973]
Large Language Models (LLMs) drive the behavior of virtual characters, allowing plots to emerge from interactions between characters and their environments.
We propose a novel plot creation workflow that mediates between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation.
The process creates "living stories" that dynamically adapt to various game world states, resulting in narratives co-created by the author, character simulation, and player.
arXiv Detail & Related papers (2024-05-17T23:04:51Z) - Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions [48.18584733906447]
This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more cohesively related than isolated.
We propose a fine-grained modeling of narrative context, by formulating a graph dubbed NarCo, which explicitly depicts task-agnostic coherence dependencies.
arXiv Detail & Related papers (2024-02-21T06:14:04Z) - Visual Storytelling with Question-Answer Plans [70.89011289754863]
We present a novel framework which integrates visual representations with pretrained language models and planning.
Our model translates the image sequence into a visual prefix, a sequence of continuous embeddings which language models can interpret.
It also leverages a sequence of question-answer pairs as a blueprint plan for selecting salient visual concepts and determining how they should be assembled into a narrative.
arXiv Detail & Related papers (2023-10-08T21:45:34Z) - M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using
Protagonist's Mental Representations [14.64546899992196]
We propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state.
We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution.
Our model is able to achieve significant improvements in the task of identifying climax and resolution.
arXiv Detail & Related papers (2023-02-18T20:48:02Z) - Mixed Multi-Model Semantic Interaction for Graph-based Narrative
Visualizations [10.193264105560862]
Narrative maps are a visual representation model that can assist analysts to understand narratives.
We present a semantic interaction framework for narrative maps that can support analysts through their sensemaking process.
We find that our SI system can model the analysts' intent and support incremental formalism for narrative maps.
arXiv Detail & Related papers (2023-02-13T15:32:10Z) - Generating Coherent Narratives by Learning Dynamic and Discrete Entity
States with a Contrastive Framework [68.1678127433077]
We extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation.
Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines.
arXiv Detail & Related papers (2022-08-08T09:02:19Z) - Towards a Formal Model of Narratives [0.0]
Our framework affords the ability to discuss key qualities of stories and their communication.
We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader.
arXiv Detail & Related papers (2021-03-23T22:33:23Z) - Narrative Maps: An Algorithmic Approach to Represent and Extract
Information Narratives [6.85316573653194]
This article combines the theory of narrative representations with the data from modern online systems.
A narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map.
Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.
arXiv Detail & Related papers (2020-09-09T18:30:44Z) - PlotMachines: Outline-Conditioned Generation with Dynamic Plot State
Tracking [128.76063992147016]
We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states.
In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative.
arXiv Detail & Related papers (2020-04-30T17:16:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.