Understanding Restaurant Stories Using an ASP Theory of Intentions
Daniela Inclezan, Qinglin Zhang, Marcello Balduccini, Ankush Israney · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2018
The paper describes an application of logic programming to story understanding. Substantial work in this direction has been done by Erik Mueller, who focused on texts about stereotypical activities (or scripts), in particular restaurant stories. His system performed well, but could not understand texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we model scripts as activities and employ the concept of an intentional agent to reason about both normal and exceptional scenarios.