Abstract We consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.

中文翻译：

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从过去的学习中获得了什么

摘要 我们考虑使系统能够将先前学到的信息应用于新情况的方法，从而最大限度地减少再培训的需要。我们表明，可以从先前的学习中传输的信息量存在理论限制，并且对不断变化的环境的鲁棒性取决于要学习的关系与基础模型的因果结构之间的微妙平衡。我们通过示例展示了如何量化这种稳健性。