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.

翻译：动作模型以前提/效果公理的形式呈现，为人工智能代理提供了动作之间的因果与动机联系。动作模型获取已被视为规划技术应用中的瓶颈，尤其在叙事规划领域。从叙事文本中自动获取动作模型至关重要，但由于此类文本固有的复杂性而极具挑战。我们提出NaRuto系统，该系统以无监督方式从叙事文本中提取结构化事件，并基于常识事件关系预测、文本矛盾性与相似性，生成规划语言风格的动作模型。经典叙事规划领域的实验结果表明，NaRuto能够生成显著优于现有全自动方法、甚至可与半自动方法相媲美的动作模型。