Simulated Perceptions for Emergent Storytelling

Abstract

Automated story generation is a desired feature in games and interactive media because it can control how a virtual world evolves so that it can be adapted to the players' choices. In order to have variety and quality in the generated stories, previous works have relied on simulation-based storytelling, in which a story is generated as their characters, represented as agents, try to achieve their goals. One challenge of this approach is to make the agents act more like human characters and less like omniscient intelligent beings. In this article, we present a perception model for simulation-based story generation that introduces errors into characters' knowledge, (mis)leading them to nonoptimal, but still coherent, believable actions. The perception is executed using a description of the virtual world's elements using physical characteristics, and a pattern matching process that associates combinations of physical characteristics with predefined combinations of attributes, which are allowed to be wrong, and consequently may result in non-perfect interpretations of the world. We developed a story generation system from the proposed model and tested it with a version of the Little Red Riding Hood story, famous for its perception failure. Our results show interesting variations for the traditional known ending.