Problem
Research questions and friction points this paper is trying to address.
Mixed-initiative Visual Analytics
State Synchronization
Human-AI Collaboration
Engineering Overhead
MIVAIS: A Study Environment for Multi-Agent Mixed-Initiative Visual Analytics Applications
🤖 AI Summary
本文介绍MIVAIS平台，旨在通过标准化人机交互与状态同步解决混合主动视觉分析系统的开发和评估难题。
📝 Abstract
Mixed-initiative Visual Analytics (VA) systems empower human users by interleaving human intuition with software agents and their machine intelligence. However, the development and rigorous evaluation of such systems remain constrained by engineering overhead. Developers must, e.g., implement complex, low-level state synchronization to manage asynchronous agent behaviors, while researchers struggle to capture the multimodal provenance required to study and evaluate human-AI collaboration. We present MIVAIS, a dual-layered research platform designed to abstract the structural complexities of mixed-initiative VA. First, it contributes a computational Infrastructure that standardizes human-software agent interaction, state synchronization, and communication between the agents. Second, it provides a declarative Study Environment that automatically logs multimodal human-AI telemetry - including application/system state, screen capture, audio, and additional sensor data - enabling seamless, in-situ user studies and post-session analysis. We technically validate our infrastructure by replicating three state-of-the-art systems (Podium, Voyager 2, and ProactiveVA). Furthermore, we evaluate the framework's expressiveness and efficiency through expert case studies with HCI and VA researchers, demonstrating how MIVAIS effectively lowers the barrier to prototyping and evaluating intelligent, co-adaptive interfaces.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Mixed-initiative Visual Analytics
State Synchronization
Multimodal Provenance
Human-AI Telemetry
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