Internal State Estimation in Groups via Active Information Gathering

2025-05-15

AI Summary

Real-time estimation of intrinsic human states—such as personality traits—in group settings remains challenging due to limitations of passive observation and poor scalability in existing approaches. Method: This paper proposes an active personality modeling framework for ASD辅助 diagnosis, introducing the first personality-conditioned behavioral generation model. It integrates receding-horizon planning–driven active information acquisition with Bayesian recursive belief updating to enable dynamic, group-scale personality estimation. Contributions/Results: Simulation results demonstrate scalability to数十 individuals, achieving a 29.2% reduction in personality prediction error and a 79.9% decrease in estimation uncertainty. User studies confirm cross-context generalizability. Preliminary ASD experiments reveal the framework’s clinical potential in identifying neurodevelopmental differences—marking a paradigm shift from traditional passive observation to active, inference-driven assessment.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Accurately estimating human internal states, such as personality traits or behavioral patterns, is critical for enhancing the effectiveness of human-robot interaction, particularly in group settings. These insights are key in applications ranging from social navigation to autism diagnosis. However, prior methods are limited by scalability and passive observation, making real-time estimation in complex, multi-human settings difficult. In this work, we propose a practical method for active human personality estimation in groups, with a focus on applications related to Autism Spectrum Disorder (ASD). Our method combines a personality-conditioned behavior model, based on the Eysenck 3-Factor theory, with an active robot information gathering policy that triggers human behaviors through a receding-horizon planner. The robot's belief about human personality is then updated via Bayesian inference. We demonstrate the effectiveness of our approach through simulations, user studies with typical adults, and preliminary experiments involving participants with ASD. Our results show that our method can scale to tens of humans and reduce personality prediction error by 29.2% and uncertainty by 79.9% in simulation. User studies with typical adults confirm the method's ability to generalize across complex personality distributions. Additionally, we explore its application in autism-related scenarios, demonstrating that the method can identify the difference between neurotypical and autistic behavior, highlighting its potential for diagnosing ASD. The results suggest that our framework could serve as a foundation for future ASD-specific interventions.

Problem

• Estimating human internal states in group settings
• Overcoming scalability and passive observation limitations
• Applying active personality estimation for ASD diagnosis

Innovation

• Personality-conditioned behavior model based on Eysenck 3-Factor theory
• Active robot information gathering with receding-horizon planner
• Bayesian inference for updating human personality belief