Non-Monotonic Reasoning and Story Comprehension
This work addresses story comprehension for AI systems, but it appears incremental as it builds on existing theories from psychology and AI argumentation.
The paper tackles the problem of integrating explicit narrative information with implicit common sense knowledge for story comprehension, proposing a framework based on argumentation theory and reporting on empirical efforts to evaluate its ability to capture human understanding variability.
This paper develops a Reasoning about Actions and Change framework integrated with Default Reasoning, suitable as a Knowledge Representation and Reasoning framework for Story Comprehension. The proposed framework, which is guided strongly by existing knowhow from the Psychology of Reading and Comprehension, is based on the theory of argumentation from AI. It uses argumentation to capture appropriate solutions to the frame, ramification and qualification problems and generalizations of these problems required for text comprehension. In this first part of the study the work concentrates on the central problem of integration (or elaboration) of the explicit information from the narrative in the text with the implicit (in the readers mind) common sense world knowledge pertaining to the topic(s) of the story given in the text. We also report on our empirical efforts to gather background common sense world knowledge used by humans when reading a story and to evaluate, through a prototype system, the ability of our approach to capture both the majority and the variability of understanding of a story by the human readers in the experiments.