Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning

Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning

Blog Article

Conventional personality assessment methods depend on subjective input, while game-based AI predictive methods offer a dynamic and objective framework.However, training these models requires large and labeled datasets, which are challenging to obtain from real players with diverse personality traits.In this paper, we propose a multi-agent system using Deep Reinforcement Learning in a game environment to generate the necessary labeled data.Each agent is trained with custom reward sony c functions based on the HiDAC system that encourages trait-aligned behaviors to emulate specific personality traits based on the OCEAN personality trait model.The Multi-Agent Posthumous Credit Assignment (MA-POCA) algorithm facilitates continuous learning, allowing agents to emulate behaviors through self-play.

The resulting gameplay data provide diverse, high-quality samples.This approach allows for robust individual and team assessments, as agent interactions reveal the impact of personality traits on team dynamics and performance.Ultimately, this methodology provides a scalable, unbiased 4 post backdrop stand methodology for human personality evaluation in various settings, establishing new standards for data-driven assessment methods.