Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
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发表时间：
2021-02
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通讯作者：
Kanishk Gandhi;Gala Stojnic;B. Lake;M. Dillon
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作者：
Kanishk Gandhi;Gala Stojnic;B. Lake;M. Dillon
为了实现类似人类的日常生活常识，机器学习系统必须理解和推理环境中其他智能体的目标、偏好和行为。到一岁结束时，人类婴儿直观地达到了这样的常识，这些认知成就为人类对他人精神状态的丰富而复杂的理解奠定了基础。机器能对其他智能体（如人类婴儿）进行可推广的常识推理吗？婴儿直觉基准（BIB）挑战机器根据其行为的根本原因来预测代理行为的可行性。由于BIB的内容和范式都来自发展认知科学，因此BIB允许直接比较人类和机器的表现。然而，最近提出的基于深度学习的代理推理模型未能显示出婴儿般的推理，这使得BIB成为一个公开的挑战。
To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of other agents in the environment. By the end of their first year of life, human infants intuitively achieve such common sense, and these cognitive achievements lay the foundation for humans' rich and complex understanding of the mental states of others. Can machines achieve generalizable, commonsense reasoning about other agents like human infants? The Baby Intuitions Benchmark (BIB) challenges machines to predict the plausibility of an agent's behavior based on the underlying causes of its actions. Because BIB's content and paradigm are adopted from developmental cognitive science, BIB allows for direct comparison between human and machine performance. Nevertheless, recently proposed, deep-learning-based agency reasoning models fail to show infant-like reasoning, leaving BIB an open challenge.