Text Mining Emergent Human Behaviors for Interactive Systems

Abstract

People engage with thousands of situations, activities, and objects on a daily basis. Hand-coding this knowledge into interactive systems is prohibitively labor-intensive, but fiction captures a vast number of human lives in moment to moment detail. In this paper, we bootstrap a knowledge graph of human activities by text mining a large dataset of modern fiction on the web. Our knowledge graph, Augur, describes human actions over time as conditioned by nearby locations, people, and objects. Applications can use this graph to react to human behavior in a data-driven way. We demonstrate an Augur-enhanced video game world in which non-player characters follow realistic patterns of behavior, interact with their environment and each other, and respond to the user's behavior.

DOI: 10.1145/2702613.2732805

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@inproceedings{Fast2015TextME, title={Text Mining Emergent Human Behaviors for Interactive Systems}, author={Ethan Fast and Pranav Rajpurkar and Michael S. Bernstein}, booktitle={CHI Extended Abstracts}, year={2015} }