An Application of ASP Theories of Intentions to Understanding Restaurant Scenarios: Insights and Narrative Corpus
This work addresses a specific problem in natural language understanding for AI researchers, but it is incremental as it refines existing methods for a narrow domain.
The paper tackled the challenge of understanding exceptional scenarios in restaurant narratives by modeling characters as intentional agents using Answer Set Programming, resulting in increased coverage and performance.
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).