Enhancing Teamwork Through Team-Level Intent Inference

摘要

Applications that have access to user intent and task context can support better, faster decision-making on the part of the user. In this paper, we present AUTOS, an approach to the implementation of individual and team intent inference. AUTOS uses observable contextual clues to infer current operator task state and predict future task state. Guided by the concepts of activity theory, AUTOS task models can be hierarchically organized to infer team intent. To illustrate some of the properties of AUTOS technology, we describe our ongoing work in team intent inference with a prototype system embedded in a demonstration application for a military domain.