Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
This work addresses the problem of creating more human-centric AI agents for interactive environments, though it is incremental in applying personality modeling to text-based games.
The study tackled the challenge of aligning artificial agents with human values by investigating how personality traits influence agent behavior in text-based games, demonstrating that agents with specific personality types, like higher Openness, show performance advantages.
Artificial agents are increasingly central to complex interactions and decision-making tasks, yet aligning their behaviors with desired human values remains an open challenge. In this work, we investigate how human-like personality traits influence agent behavior and performance within text-based interactive environments. We introduce PANDA: Personality Adapted Neural Decision Agents, a novel method for projecting human personality traits onto agents to guide their behavior. To induce personality in a text-based game agent, (i) we train a personality classifier to identify what personality type the agent's actions exhibit, and (ii) we integrate the personality profiles directly into the agent's policy-learning pipeline. By deploying agents embodying 16 distinct personality types across 25 text-based games and analyzing their trajectories, we demonstrate that an agent's action decisions can be guided toward specific personality profiles. Moreover, certain personality types, such as those characterized by higher levels of Openness, display marked advantages in performance. These findings underscore the promise of personality-adapted agents for fostering more aligned, effective, and human-centric decision-making in interactive environments.