Felt: A simple story sifter

Abstract

Story sifting, also known as story recognition, has been identified as one of the major design challenges currently facing interactive emergent narrative. However, despite continued interest in emergent narrative approaches, there has been relatively little work in the area of story sifting to date, leaving it unclear how a story sifting system might best be implemented and what challenges are likely to be encountered in the course of implementing such a system. In this paper, we present Felt, a simple query language-based story sifter and rules-based simulation engine that aims to serve as a first step toward answering these questions. We describe Felt’s architecture, discuss several design case studies of interactive emergent narrative experiences that make use of Felt, reflect on what we have learned from working with Felt so far, and suggest directions for future work in the story sifting domain.