叙事策划：平衡情节与人物
Narrative Planning: Balancing Plot and Character
摘要：
叙事，特别是讲故事，是人类经验的重要组成部分。因此，能够推理叙事的计算系统可以成为更有效的沟通者、娱乐者、教育者和培训者。计算叙事推理的核心挑战之一是叙事生成，即自动创建有意义的事件序列。有许多因素--逻辑和美学--有助于叙事作品的成功。这种成功的核心是它的可理解性。我们认为，以下两个属性的叙事是普遍的：（a）逻辑因果关系的情节进展，（b）字符的可信度。角色可信度是观众对角色所做的动作不会对观众的怀疑产生负面影响的感知。具体来说，角色必须被观众感知为有意的代理人。在这篇文章中，我们将探讨使用细化搜索作为解决叙事生成问题的一种技术-找到一个声音和可信的字符动作序列，将初始世界状态转换为目标命题持有的世界状态。我们描述了一种新的细化搜索规划算法-基于意图的偏序因果链接（IPOCL）规划器-，除了创建因果关系健全的情节进展，通过确定可能的字符目标，解释他们的行动和创建计划结构，解释为什么这些字符致力于他们的目标，字符的意向性的原因。我们提出的实证评估的结果表明，IPOCL算法生成的叙事计划支持观众理解的字符意图比传统的偏序规划师生成的计划。
英文摘要：
Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors -- logical and aesthetic -- that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audiences suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem -- to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL) planner -- that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
