论文标题
要了解机器如何学习因果过度手术
Towards Understanding How Machines Can Learn Causal Overhypotheses
论文作者
论文摘要
机器学习和认知科学的最新工作表明，了解因果信息对于智力的发展至关重要。认知科学的广泛文献使用``Blicket ottecter''环境表明，儿童擅长于许多因果推理和学习。我们建议将该环境适应机器学习代理。当前机器学习算法的关键挑战之一是建模和理解因果疏调：关于因果关系集的可转移抽象假设。相比之下，即使是幼儿也会自发学习和使用因果关系。在这项工作中，我们提出了一个新的基准 - 一种灵活的环境，可以评估可变因果溢出物下的现有技术 - 并证明许多现有的最新方法在这种环境中概括。该基准的代码和资源可在https://github.com/cannylab/casual_overhypothess上获得。
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature in cognitive science using the ``blicket detector'' environment shows that children are adept at many kinds of causal inference and learning. We propose to adapt that environment for machine learning agents. One of the key challenges for current machine learning algorithms is modeling and understanding causal overhypotheses: transferable abstract hypotheses about sets of causal relationships. In contrast, even young children spontaneously learn and use causal overhypotheses. In this work, we present a new benchmark -- a flexible environment which allows for the evaluation of existing techniques under variable causal overhypotheses -- and demonstrate that many existing state-of-the-art methods have trouble generalizing in this environment. The code and resources for this benchmark are available at https://github.com/CannyLab/casual_overhypotheses.
