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Evolving Agents: Interactive Simulation of Dynamic and Diverse Human Personalities

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arxiv 2404.02718 v3 pith:IEIGIT3G submitted 2024-04-03 cs.HC

classification cs.HC
keywords agentspersonalitybehaviorevolutionevolvingsimulationagentbelievable
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Human-like Agents with diverse and dynamic personalities could serve as an essential design probe in the process of user-centered design, thereby enabling designers to enhance the user experience of interactive applications. In this article, we introduce Evolving Agents, a novel agent architecture that consists of two systems: Personality and Behavior. The Personality system includes Cognition, Emotion, and Character Growth modules. The Behavior system comprises two modules: Planning and Action. We also build a simulation platform that enables agents to interact with the environment and other agents. Evolving Agents can simulate the human personality evolution process. Compared to its initial state, agents' personality and behavior patterns undergo believable development after several days of simulation. Agents reflect on their behavior to reason and develop new personality traits. These traits, in turn, generate new behavior patterns, forming a feedback loop-like personality evolution. Our experiment utilized a simulation platform with ten agents for evaluation. During the assessment, these agents experienced believable and inspirational personality evolution. Through ablation and control experiments, we demonstrated the effectiveness of agent personality evolution, and all of our agent architecture modules contribute to creating believable human-like agents with diverse and dynamic personalities. We also demonstrated through workshops how Evolving Agents could inspire designers.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Step-Level Preference Learning for Generative Agents in Social Simulations

    cs.AI 2026-07 conditional novelty 7.0 of 10

    Step-level human preference data collected via SimPref, then SFT+DPO, improves long-horizon social-simulation behavior of open-weight LLM agents on held-out events.

  2. Simulating Human Behavior with the Psychological-mechanism Agent: Integrating Feeling, Thought, and Action

    cs.HC 2025-06 reject novelty 5.0 of 10

    PSYA combines ALMA emotion layers and the Triple Network Model to make LLM agents behave more human-like and reproduce several classic psychology experiment results.

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