An ASP Methodology for Understanding Narratives about Stereotypical Activities
This work addresses a specific challenge in natural language understanding for AI systems, focusing on narrative comprehension in domains like restaurant scenarios, and is incremental as it builds on existing script-based approaches.
The paper tackles the problem of understanding narratives about stereotypical activities, particularly handling exceptional scenarios where prior methods failed, by proposing a methodology using Answer Set Programming with activities and intentional agents, demonstrated through question answering on restaurant stories.
We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities as scripts. His systems were able to understand a good number of narratives, but could not process 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 substitute scripts by activities (i.e., hierarchical plans associated with goals) and employ the concept of an intentional agent to reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.