Scientific models describe natural phenomena at different levels of
abstraction. Abstract descriptions can provide the basis for interventions on
the system and explanation of observed phenomena at a level of granularity that
is coarser than the most fundamental account of the system. Beckers and Halpern
(2019), building on work of Rubenstein et al. (2017), developed an account of
abstraction for causal models that is exact. Here we extend this account to the
more realistic case where an abstract causal model offers only an approximation
of the underlying system. We show how the resulting account handles the
discrepancy that can arise between low- and high-level causal models of the
same system, and in the process provide an account of how one causal model
approximates another, a topic of independent interest. Finally, we extend the
account of approximate abstractions to probabilistic causal models, indicating
how and where uncertainty can enter into an approximate abstraction.

本文从科学模型、抽象、因果模型、近似抽象和概率因果模型等方面出发，对因果模型的抽象方法进行了详细分析，平衡了低层和高层因果模型之间出现的差异，并在此基础上进行了扩展。

近似因果抽象

Approximate Causal Abstraction

The combinatorial explosion that plagues planning and reinforcement learning
(RL) algorithms can be moderated using state abstraction. Prohibitively large
task representations can be condensed such that essential information is
preserved, and consequently, solutions are tractably computable. However, exact
abstractions, which treat only fully-identical situations as equivalent, fail
to present opportunities for abstraction in environments where no two
situations are exactly alike. In this work, we investigate approximate state
abstractions, which treat nearly-identical situations as equivalent. We present
theoretical guarantees of the quality of behaviors derived from four types of
approximate abstractions. Additionally, we empirically demonstrate that
approximate abstractions lead to reduction in task complexity and bounded loss
of optimality of behavior in a variety of environments.

本研究探讨了使用状态抽象来减轻计划和强化学习算法中的组合爆炸问题。我们研究了近似状态抽象的理论保证和在各种环境中的有效性。结果表明，使用近似抽象可以减少任务复杂度和行为最优性的损失。