Automated Extension of Narrative Planning Domains with Antonymic Operators
Automated Extension of Narrative Planning Domains with Antonymic Operators
复制标题
使用反义运算符自动扩展叙事规划域
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Cavazza
中科院分区：
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作者：
J. Porteous;A. Lindsay;Jonathon Read;M. Truran;M. Cavazza
人工智能规划已被广泛用于叙事生成和交互式讲故事中虚拟演员的控制。这种动态环境的规划模型必须包括替代行动，使偏离基线故事情节，以产生多个故事变量，并能够响应可能对故事世界的变化。然而，这些领域模型的实际创建在很大程度上是一个经验性的过程，缺乏对替代行动定义的原则性方法。我们的工作已经解决了这个问题，在本文中，我们提出了一种新的自动化方法，从现有的非交互式版本的交互式叙事域模型的生成。其核心是在半自动生产的原则性机制内使用与形成基线图的行动相反的行动。重要的是，这种新创建的域内容仍然应该是人类可读的，并且为此，使用从一系列在线词汇资源中选择的反义词自动生成新动作和谓词的标签。我们的方法是完全实现在一个原型系统和它的潜力，通过正式的实验评估和用户评价生成的动作标签证明。
AI Planning has been widely used for narrative generation and the control of virtual actors in interactive storytelling. Planning models for such dynamic environments must include alternative actions which enable deviation away from a baseline storyline in order to generate multiple story variants and to be able to respond to changes that might be made to the story world. However, the actual creation of these domain models has been a largely empirical process with a lack of principled approaches to the definition of alternative actions. Our work has addressed this problem and in the paper we present a novel automated method for the generation of interactive narrative domain models from existing non-interactive versions. Central to this is the use of actions that are contrary to those forming the baseline plot within a principled mechanism for their semi-automatic production. It is important that such newly created domain content should still be human-readable and to this end labels for new actions and predicates are generated automatically using antonyms selected from a range of on-line lexical resources. Our approach is fully implemented in a prototype system and its potential demonstrated via both formal experimental evaluation and user evaluation of the generated action labels.