Automated Action Model Acquisition from Narrative Texts
Ruiqi Li Leyang Cui Songtuan Lin Patrik Haslum
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.