Human-Computer Collaboration for Visual Analytics: an Agent-based Framework

TL;DR

This paper introduces an agent-based conceptual framework for visual analytics that unifies various analytic processes and supports mixed-initiative, guided, and collaborative analysis by modeling humans, environments, and AI agents.

Contribution

It presents a novel, unified agent-based model for visual analytics, bridging human and AI collaboration and extending existing conceptual models.

Findings

Framework unifies visual analytic processes.

Enables reasoning about collaborative and guided analysis.

Characterizes analysts, settings, and guidance through agents.

Abstract

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever-expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent-based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed-initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.