Extroversion-Introversion Rescheduler in Generative Agent via Few-Shot Prompting

Abstract

Generative Agent (GA) has emerged as a promising framework for simulating human-like behaviors. However, it is required for GA to generate a schedule that consistently reflects the agent's E-I trait particularly in the extroversion-introversion (E-I) category to improve the realism of GA. We propose an E-I evaluation and rescheduling method that adjusts the agent's schedule. Specifically, our method takes as input a one-hour schedule segmented into five-minute tasks and a corresponding E-I trait classified into seven degrees ranging from extremely high extroversion to extremely high introversion. Using the Evaluator powered by GPT-4o mini, each task is assessed for the alignment with the E-I traits. Each task that fails to meet a threshold is regenerated using few-shot prompting based on a collected successful schedule. This process is repeated until all tasks are aligned with the corresponding traits. Finally, the evaluator accesses the overall E-I consistency of the schedule that contains the tasks. Therefore, it is possible for the proposed method to enable E-I-consistent schedule generation in GA without retraining any models. In experiments, the proposed framework improved E-I alignment from an average of 14.7% to that of 78.4% with only 1.38 iterations on average, demonstrating both practical effectiveness and computational efficiency.