A computational approach to eliciting and modeling stories with social interactions
Date
2013
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
In this work, we explored a middle point between the common approach of
sharing knowledge using oral narratives and the automatic approach of generation of
narratives by computational tools. We describe a new model to capture the
flow of
events of a story and the changes in the social interactions between the characters
as the story progresses. We present a description of the software implementation of
the model. Also, we provide a discussion about the design and implementation of
the computational tool used to capture stories with the proposed model. The main
motivation of this work lies on the fact that each domain of knowledge has people
with a wide range of experience and expertise in that domain. Also, within each
domain people use narratives as an e ffective medium of transferring their knowledge.
A crucial point is that individuals of a domain have the potential of making valuable
contributions to the body of knowledge and that those contributions are often driven
in a narrative fashion. In addition, as interactive entertainment continues its role
as a pervasive element of today's culture, the potential of a meaningful experience
through the use of narratives can only be achieved if there are tools that aid a wider
audience in the creation of those narratives. The AI community has recognized the
overwhelming task of authoring stories for interactive entertainment, which demands
expertise in computational models for the structure of the story and its execution, as
well as expertise in creating the content of the story. Thus, establishing a computational
representation of narratives that aid individuals to share their experience and expertise
could open the door for capturing the underlying knowledge of a domain.