Action models, which take the form of precondition/effect axioms, facilitate
causal and motivational connections between actions for AI agents. Action model
acquisition has been identified as a bottleneck in the application of planning
technology, especially within narrative planning. Acquiring action models from
narrative texts in an automated way is essential, but challenging because of
the inherent complexities of such texts. We present NaRuto, a system that
extracts structured events from narrative text and subsequently generates
planning-language-style action models based on predictions of commonsense event
relations, as well as textual contradictions and similarities, in an
unsupervised manner. Experimental results in classical narrative planning
domains show that NaRuto can generate action models of significantly better
quality than existing fully automated methods, and even on par with those of
semi-automated methods.

通过预测常识事件关系以及文本中的矛盾和相似之处，NaRuto 以无监督的方式从叙事文本中提取结构化事件，并生成基于计划语言风格的动作模型，实验结果显示在经典叙事规划领域中，NaRuto 能够生成比现有的全自动方法质量更好的动作模型，甚至与半自动方法相媲美。