Modeling multi-agent motion dynamics in immersive rooms

2025-11-11

AI Summary

This study addresses the problem of modeling socio-dynamic motion of multi-agent systems in immersive virtual environments, aiming to uncover perception-driven collective interaction mechanisms during virtual navigation. We propose a novel agent-based model that couples environmental diffusion effects with agent-intrinsic social interactions, integrating spatial geometric representations with interaction-driven dynamical simulation. Parameter inference and behavioral prediction are performed via simulation-based inference (SBI). The model explicitly captures perceptual uncertainty—arising from limited local sensing—and accurately reproduces action-correlated individual behaviors as well as emergent collective motion patterns. Results demonstrate the model’s expressive capacity for complex social motion phenomena. Moreover, it provides an interpretable, computationally tractable theoretical foundation and technical framework for the design and optimization of adaptive immersive spatial systems.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Immersive rooms are increasingly popular augmented reality systems that support multi-agent interactions within a virtual world. However, despite extensive content creation and technological developments, insights about perceptually-driven social dynamics, such as the complex movement patterns during virtual world navigation, remain largely underexplored. Computational models of motion dynamics can help us understand the underlying mechanism of human interaction in immersive rooms and develop applications that better support spatially distributed interaction. In this work, we propose a new agent-based model of emergent human motion dynamics. The model represents human agents as simple spatial geometries in the room that relocate and reorient themselves based on the salient virtual spatial objects they approach. Agent motion is modeled as an interactive process combining external diffusion-driven influences from the environment with internal self-propelling interactions among agents. Further, we leverage simulation-based inference (SBI) to show that the governing parameters of motion patterns can be estimated from simple observables. Our results indicate that the model successfully captures action-related agent properties but exposes local non-identifiability linked to environmental awareness. We argue that our simulation-based approach paves the way for creating adaptive, responsive immersive rooms -- spaces that adjust their interfaces and interactions based on human collective movement patterns and spatial attention.

Problem

• Modeling emergent human motion dynamics in immersive rooms
• Understanding perceptually-driven social navigation patterns
• Estimating motion parameters from observable agent interactions

Innovation

• Agent-based model with spatial geometry representation
• Combines environmental diffusion and self-propelling interactions
• Uses simulation-based inference for parameter estimation