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Computational Solutions To The Exponential Problem in Longform Branching Interactive Digital Narratives

长分支交互式数字叙事中指数问题的计算解决方案

基本信息

批准号：
2480958
负责人：
金额：
--
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2480958
关键词：
Computational Solutions Exponential Problem Longform

项目摘要

The proposal is to explore computational approaches to solving the exponential problem for content creation in branching digital narratives. Branching is one popular way of allowing player autonomy to be expressed in an interactive experience (whether it be a kinetic novel formed mainly of text, or a video game). In a branching narrative, each time a branch is added to an existing branch, it increases the workload of authorship exponentially. If a story has two branches, and each of those branches has two branches and so on, after just ten choices, 1024 nodes would need to be written. This exponential problem strongly limits the amount of compounded branching that can occur in a narrative.There are a number of existing structural solutions to this problem which can be modelled. These include structures that frequently merge after branching, or use state tracking for delayed branching. These approaches can be seen in most existing branching narratives. Beyond these structural solutions, there are also promising computational approaches. Using an AI narrative director is one possibility. Another is setting up procedural rules for characters and environments so narratives can emerge in a more sandbox environment. The intention is to research the various existing solutions to the branching problem, and explore how generative techniques might extend them.

该提案旨在探索计算方法来解决分支数字叙事中内容创作的指数问题。分支是一种流行的方式，允许玩家在交互体验中表达自主性（无论是主要由文本组成的动态小说还是视频游戏）。在分支叙述中，每当一个分支被添加到一个现有的分支中时，它会成倍地增加作者的工作量。如果一个故事有两个分支，每个分支又有两个分支，依此类推，在十个选择之后，需要写1024个节点。这个指数问题极大地限制了叙事中可能出现的复合分支的数量。有一些现有的结构解决方案可以模拟这个问题。这些包括在分支后频繁合并的结构，或者使用状态跟踪来延迟分支。这些方法可以在大多数现有的分支叙述中看到。除了这些结构的解决方案，也有很有前途的计算方法。使用AI叙事导演是一种可能性。另一个是为角色和环境设置程序规则，这样叙事就可以在一个更沙箱化的环境中出现。目的是研究分支问题的各种现有解决方案，并探索生成技术如何扩展它们。