人格动力学：揭示性格特征对基于文本的游戏中智能体的影响
Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
AI总结：
本研究提出PANDA方法，通过训练性格分类器并将性格特征整合到策略学习流程中，使智能体在文本游戏中展现特定性格，发现某些性格类型（如高开放性）能显著提升性能，为构建更符合人类价值观的智能体提供了新途径。
摘要：
人工智能体在复杂交互和决策任务中日益占据核心地位，然而使其行为与期望的人类价值观保持一致仍是一个开放性挑战。在本研究中，我们探讨了类人性格特征如何影响智能体在基于文本的交互环境中的行为与表现。我们提出了 PANDA: Personality Adapted Neural Decision Agents，这是一种将人类性格特征投射到智能体上以指导其行为的新方法。为了在基于文本的游戏智能体中引入性格，我们训练了一个性格分类器来识别智能体行为所表现出的性格类型，并将性格特征直接整合到智能体的策略学习流程中。通过在25个基于文本的游戏中部署体现16种不同性格类型的智能体并分析其轨迹，我们证明智能体的行动决策可以被引导至特定的性格特征。此外，某些性格类型，如以较高开放性为特征的类型，在性能上表现出显著优势。这些发现突显了性格适应型智能体在促进交互环境中更加一致、有效且以人为本的决策方面的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
