给AI Agent注入人格特质，让其行为更符合人类预期。
Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
- 用人格分类器和策略学习结合，让Agent表现出特定人格。
- 高开放性人格的Agent在25个游戏中表现更优。
- 适合想让AI更人性化、贴近人类决策的研究者。
人工智能代理在复杂交互与决策任务中日益重要，但使其行为与人类价值观对齐仍是挑战。本文研究人类人格特质如何影响文本游戏中的代理行为与表现。提出PANDA：人格适配神经决策代理，通过训练人格分类器识别代理行为所体现的人格类型，并将人格特征直接融入代理策略学习流程。在25个文本游戏环境中部署16种不同人格类型的代理，分析其行为轨迹，证明可有效引导代理决策趋向特定人格。其中，开放性较高的性格在性能上显著占优。结果表明，人格适配代理在构建更契合人类、高效且以人为核心互动环境方面具有潜力。
原文摘要 · Abstract (English)
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.
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