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

2025-04-09

AI Summary

This study investigates how personality traits influence agent behavior and decision-making efficacy in text-based interactive environments. To address this, we propose the PANDA framework—the first systematic approach to embedding 16 personality types derived from the Big Five model into reinforcement learning agents. PANDA jointly optimizes a differentiable personality classifier and a policy network, enabling controllable, personality-aware behavioral modeling. Our method integrates personality embeddings, policy gradient learning, and the TextWorld textual game environment, and is empirically validated across 25 diverse text games. Results demonstrate that agents with high openness achieve an average 19.3% improvement in task completion rate, confirming that personality traits significantly affect decision quality and human–AI alignment. This work establishes an interpretable, tunable, and embodied paradigm for personality-infused AI agents.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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: PersonalityAdapted 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.

Problem

• Aligning agent behaviors with human values in interactive tasks
• Influencing agent behavior via human-like personality traits
• Enhancing agent performance using personality-adapted decision-making

Innovation

• PANDA method for personality-adapted neural agents
• Integrate personality profiles into policy-learning
• Personality classifier guides agent behavior