To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of others. Human infants intuitively achieve such common sense by making inferences about the underlying causes of other agents' actions. Directly informed by research on infant cognition, our benchmark BIB challenges machines to achieve generalizable, common-sense reasoning about other agents like human infants do. As in studies on infant cognition, moreover, we use a violation of expectation paradigm in which machines must predict the plausibility of an agent's behavior given a video sequence, making this benchmark appropriate for direct validation with human infants in future studies. We show that recently proposed, deep-learning-based agency reasoning models fail to show infant-like reasoning, leaving BIB an open challenge.

机器学习系统如何理解和推断环境中其他代理的目标、偏好和行动，是实现人类常识的关键。虽然基于深度学习的代理推理模型日益成熟，但在类似人类婴儿的推理上，仍有待突破。Baby Intuitions Benchmark (BIB) 作为发展性认知科学的研究成果，挑战机器的类人类推理能力，可用于与人类推理能力的直接比较。

婴儿直觉基准（BIB）：辨别他人的目标、偏好和行为