Automated Action Model Acquisition from Narrative Texts
This addresses a bottleneck in planning technology for AI agents, particularly in narrative planning, by automating model acquisition from complex texts, though it is incremental as it builds on existing methods.
The paper tackles the problem of automatically acquiring action models from narrative texts, presenting NaRuto, an unsupervised system that extracts events and generates planning-language-style action models, achieving significantly better quality than fully automated methods and comparable to semi-automated ones in classical narrative planning domains.
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.