A top-down design methodology based on causality and chronology for developing assisted story generation systems

Assisted story generation systems do not include automatic generation of content due to the fact that creating generation algorithms is a challenging task, usually carried out in research projects on Artificial Intelligence. This paper proposes a top-down design methodology in which the knowledge representation is based on causality and chronology. The proposed methodology partially eases the process by dividing the narrative generation system in two parts: a generic engine and a domain definition based only on a specific set of predicates. The theoretical model and an implemented case study with resulting stories are presented.