Finding action dependencies using the crowd

Training intelligent systems is a time-consuming and costly process that often limits real-world applications. Prior work has attempted to compensate for this challenge by generating sets of labeled training data for machine learning algorithms using affordable human contributors. In this paper, we present ARchitect, a system that uses the crowd to extract context-dependent relational structure. We focus on activity recognition because of its broad applicability, high level of variation, and difficulty of training systems a priority. We demonstrate that using our approach, the crowd can accurately and consistently identify relationships between actions even over sessions containing different workers and varied executions of an activity. This results in the ability to identify multiple valid execution paths from a single observation, suggesting that one-off learning can be facilitated by using the crowd as an on-demand source of human intelligence in the knowledge acquisition process.