The Evolution of Interpretive Contexts in Stories

Abstract

Modeling the effect of context on interpretation, for the purposes of building intelligent systems, has been a long-standing problem: qualities of logic can restrict accurate contextual interpretation, even when there is only one context to consider. Stories offer a range of structures that could extend formal theories of context, indicating how arrays of inferred contexts are able to knit together, making an ontological reference that is specific to the particular set of circumstances embodied in the tale. This derived ontology shifts as the text unfolds, enabling constant revision and the emergence of unexpected meanings. The described approach employs dynamic knowledge representation techniques to model how these structures are built and changed. Two new operators have been designed for this purpose: governance and causal conceptual agents. As an example, a few lines from the story Red Riding Hood As a Dictator Would Tell It are used to demonstrate how a story interpretive framework can be continually re-made, in a way that produces unexpected interpretations of terms.