A Hybrid Model for Novel Story Generation Using the Affective Reasoner and ChatGPT

In this paper a hybrid model is presented for generating novel stories using (a) a traditional symbolic AI cognitive-appraisal model of emotions embodied in the Affective Reasoner (AR), and (b) the large-language-model-based (LLM) system embodied in ChatGPT. The novel emotion and narrative structure is generated first by AR techniques—giving strong, symbolic computable structure to the intermediate narratives—and then fed in series to ChatGPT to add complementary world knowledge and elegant language structure. The resulting stories are polished and cohesive, but the basic structural elements remain under computational control. Explanations about content can be generated, based on the emotion content, and also on the appraisal-based dispositions, expressive temperaments, reasoning about the fortunes of others, relationships and moods of the characters in the stories. Background emotion theory is reviewed, relevant to the morphing of narratives, composed of 28 emotion categories, 24 emotion intensity variables, and ~400 channels for emotion expression, which has been implemented in the AR. A series of hybrid-generated stories are presented illustrating how the emotion makeup of characters, their emotions, their actions and their narrative perspectives remain not only consistent but are largely enhanced after treatment by ChatGPT. Actual examples of generated stories covering a wide range of complex emotion scenarios are given.