Iris: A Conversational Agent for Complex Tasks

Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, Michael S. Bernstein
CHI: ACM Conference on Human Factors in Computing Systems, 2018
Today, most conversational agents are limited to simple tasks supported by standalone commands, such as getting directions or scheduling an appointment. To support more complex tasks, agents must be able to generalize from and combine the commands they already understand. This paper presents a new approach to designing conversational agents inspired by linguistic theory, where agents can execute complex requests interactively by combining commands through nested conversations. We demonstrate this approach in Iris, an agent that can perform open-ended data science tasks such as lexical analysis and predictive modeling. To power Iris, we have created a domain-speci c language that trans- forms Python functions into combinable automata and regulates their combinations through a type system. Running a user study to examine the strengths and limitations of our approach, we nd that data scientists completed a modeling task 2.6 times faster with Iris than with Jupyter Notebook.