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Today's conversational agents are restricted to simple standalone commands.In this paper, we present Iris, an agent that draws on human conversationalstrategies to combine commands, allowing it to perform more complex tasks thatit has not been explicitly designed to support: for example, composing onecommand to "plot a histogram" with another to first "log-transform the data".To enable this complexity, we introduce a domain specific language thattransforms commands into automata that Iris can compose, sequence, and executedynamically by interacting with a user through natural language, as well as aconversational type system that manages what kinds of commands can be combined.We have designed Iris to help users with data science tasks, a domain thatrequires support for command combination. In evaluation, we find that datascientists complete a predictive modeling task significantly faster (2.6 timesspeedup) with Iris than a modern non-conversational programming environment.Iris supports the same kinds of commands as today's agents, but empowers usersto weave together these commands to accomplish complex goals.

Iris: A Conversational Agent for Complex Tasks