  Although there have been approaches that are capable of learning action
models from plan traces, there is no work on learning action models from
textual observations, which is pervasive and much easier to collect from
real-world applications compared to plan traces. In this paper we propose a
novel approach to learning action models from natural language texts by
integrating Constraint Satisfaction and Natural Language Processing techniques.
Specifically, we first build a novel language model to extract plan traces from
texts, and then build a set of constraints to generate action models based on
the extracted plan traces. After that, we iteratively improve the language
model and constraints until we achieve the convergent language model and action
models. We empirically exhibit that our approach is both effective and
efficient.
