  This paper presents a library of commonsense knowledge, RestKB, developed in
modular action language ALM and containing background knowledge relevant to the
understanding of restaurant narratives, including stories that describe
exceptions to the normal unfolding of such scenarios. We highlight features
that KR languages must possess in order to be able to express pertinent
knowledge, and expand action language ALM as needed. We show that encoding the
knowledge base in ALM facilitates its piecewise construction and testing, and
improves the generality and quality of the captured information, in comparison
to an initial ASP encoding. The knowledge base was used in a system for
reasoning about stereotypical activities, evaluated on the restaurant domain.
