Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
Ethan Fast, William McGrath, Pranav Rajpurkar, Michael Bernstein

TL;DR
This paper introduces Augur, a knowledge base mined from fiction that enables systems to predict human activities and adapt behaviors, improving human-computer interaction without manual programming.
Contribution
Augur demonstrates how analyzing over one billion words of fiction can create a broad, predictive knowledge base for human activities, advancing interactive system capabilities.
Findings
Achieved 96% recall and 71% precision in activity prediction
94% of predictions rated sensible by human judges
Enabled systems to adapt behaviors based on predicted activities
Abstract
From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
