ALIDA: Using machine learning for intent discernment in visual analytics interfaces

In this paper, we introduce ALIDA, an Active Learning Intent Discerning Agent for visual analytics interfaces. As users interact with and explore data in a visual analytics environment they are each developing their own unique analytic process. The goal of ALIDA is to observe and record the human-computer interactions and utilize these observations as a means of supporting user exploration; ALIDA does this by using interaction to make decision about user interest. As such, ALIDA is designed to track the decision history (interactions) of a user. This history is then utilized to enhance the user's decision-making process by allowing the user to return to previously visited search states, as well as providing suggestions of other search states that may be of interest based on past exploration modalities. The agent passes these suggestions (or decisions) back to an interactive visualization prototype, and these suggestions are used to guide the user, either by suggesting searches or changes to the visualization view. Current work has tested ALIDA under the exploration of homonyms for users wishing to explore word linkages within a dictionary. Ongoing work includes using ALIDA to guide users in transfer function design for volume rendering within scientific gateways.