An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents
This addresses the challenge of creating believable and interactive affective agents in video games, though it appears incremental as it builds on existing LLM capabilities.
The study tackled the problem of simulating human emotions in digital agents by proposing a chain-of-emotion architecture based on psychological appraisal, and it outperformed standard LLM architectures on user experience and content analysis metrics.
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.