mlsanityData to 14 Oct 2026

Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games

arXiv 2504.06868 ↗First posted 9 Apr 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 9 Apr 2025.

Builds on

20 of 46 references.

References · 20

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8 more
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