An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language
  Model Game Agents
Maximilian Croissant Madeleine Frister Guy Schofield Cade McCall
The development of believable, natural, and interactive digital artificial
agents is a field of growing interest. Theoretical uncertainties and technical
barriers present considerable challenges to the field, particularly with
regards to developing agents that effectively simulate human emotions. Large
language models (LLMs) might address these issues by tapping common patterns in
situational appraisal. In three empirical experiments, this study tests the
capabilities of LLMs to solve emotional intelligence tasks and to simulate
emotions. It presents and evaluates a new chain-of-emotion architecture for
emotion simulation within video games, based on psychological appraisal
research. Results show that it outperforms standard LLM architectures on a
range of user experience and content analysis metrics. This study therefore
provides early evidence of how to construct and test affective agents based on
cognitive processes represented in language models.