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


The visual analytics community has long been aiming to better understand users and assist them in their analytic endeavours. As a result, there are numerous conceptual models of visual analytics that 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 us with 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. We believe our simple yet robust framework will unify the entire 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.

Computer Graphics Forum (Proceedings of EuroVis 2023)