Abstract
In this paper a theoretical model is presented for the automated generation of plot-consistent, emotion-centered, novel narratives based on a cognitive-appraisal model of emotions. The background 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 an AI program called the Affective Reasoner (AR). Extending previous work on generated narratives, examples are given of the specification of Goals, Standards, Preferences (GSPs) and Relationships for Affective Reasoning agents within the context of a base plot. These, in turn, are shown to be used in the automated generation of AR personalities suitable for inhabiting the base plot, and capable of a wide range of emotional responses within the constraints of this same original narrative. In this way millions of plausible story variations can be generated by the AR with a wide range of themes. Generation of the fortunes-of-others emotions is emphasized as part of narrative generation. In this scheme, AR agents, in recursive psychological mind reading use beliefs about the GSPs of other agents with whom they have relationships to form their own responsive emotions.
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Notes
- 1.
A human does not always express an emotion. Sometimes, for example, emotions are suppressed, or repressed.
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Elliott, C. (2023). The Unfortunate Footnote: Using the Affective Reasoner to Generate Fortunes-of-Others Emotions in Story-Morphs. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_51
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