  A hallmark of human intelligence is the ability to ask rich, creative, and
revealing questions. Here we introduce a cognitive model capable of
constructing human-like questions. Our approach treats questions as formal
programs that, when executed on the state of the world, output an answer. The
model specifies a probability distribution over a complex, compositional space
of programs, favoring concise programs that help the agent learn in the current
context. We evaluate our approach by modeling the types of open-ended questions
generated by humans who were attempting to learn about an ambiguous situation
in a game. We find that our model predicts what questions people will ask, and
can creatively produce novel questions that were not present in the training
set. In addition, we compare a number of model variants, finding that both
question informativeness and complexity are important for producing human-like
questions.
