Why Visualization is an AI-Complete Problem (and why that matters)

Artificial Intelligence (AI) has infiltrated almost every scientific and social endeavour, including everything from medical research to the sociology of crowd control. But the foundation of AI continues to be based on digital representations of knowledge, and computational reasoning therewith. Because so much of modern knowledge infrastructure and social behaviour is connected to AI, understanding the role of AI in each such endeavour not only helps accelerate progress in those fields in which it applies, but also creates the challenges to extend the foundation for modern AI methods. The simple hypothesis herein is that so-called AI-complete problems have a role in helping to articulate the appropriate integration of AI within other disciplines. With the current growth of interest in "big data" and visualization, we argue that relatively simple formal structures provide a basis for the claim that visualization is an AI-complete problem. The value of confirming this claim is largely to encourage stronger formalizations of the visualization process in terms of the AI foundations of representation and reasoning. This connection will help ensure that relevant components of AI are appropriately applied and integrated, to provide value for a basis of a theory of visualization. The sketch of this claim here is based on the simple idea that visualization is an abstraction process, and that abstractions from partial information, however voluminous, directly confronts the non monotonic reasoning challenge; thus the need for caution in engineering visualization systems without carefully considering the consequences of visual abstraction. This is particularly important with interactive visualization, which has recently formed the basis for such fields as visual analytics.