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.

本文引入了一个认知模型来构建类似于人类一样的问题，并利用概率分布来选择简洁的程序来帮助该模型在当前情境下学习。通过模拟人类在学习模糊情况下所提出的无尽问题，我们发现该模型可以准确地预测人类所提出的问题，并能够创造性地产生训练集中不存在的新问题。此外，根据不同的模型变量，我们发现问题的信息量和复杂性对于生成类似于人类的问题至关重要。

问题提问作为程序生成