  This paper presents a practical application of Answer Set Programming to the
understanding of narratives about restaurants. While this task was investigated
in depth by Erik Mueller, exceptional scenarios remained a serious challenge
for his script-based story comprehension system. We present a methodology that
remedies this issue by modeling characters in a restaurant episode as
intentional agents. We focus especially on the refinement of certain components
of this methodology in order to increase coverage and performance. We present a
restaurant story corpus that we created to design and evaluate our methodology.
Under consideration in Theory and Practice of Logic Programming (TPLP).
