We introduce Explanatory Learning (EL), a framework to let machines use
existing knowledge buried in symbolic sequences -- e.g. explanations written in
hieroglyphic -- by autonomously learning to interpret them. In EL, the burden
of interpreting symbols is not left to humans or rigid human-coded compilers,
as done in Program Synthesis. Rather, EL calls for a learned interpreter, built
upon a limited collection of symbolic sequences paired with observations of
several phenomena. This interpreter can be used to make predictions on a novel
phenomenon given its explanation, and even to find that explanation using only
a handful of observations, like human scientists do. We formulate the EL
problem as a simple binary classification task, so that common end-to-end
approaches aligned with the dominant empiricist view of machine learning could,
in principle, solve it. To these models, we oppose Critical Rationalist
Networks (CRNs), which instead embrace a rationalist view on the acquisition of
knowledge. CRNs express several desired properties by construction, they are
truly explainable, can adjust their processing at test-time for harder
inferences, and can offer strong confidence guarantees on their predictions. As
a final contribution, we introduce Odeen, a basic EL environment that simulates
a small flatland-style universe full of phenomena to explain. Using Odeen as a
testbed, we show how CRNs outperform empiricist end-to-end approaches of
similar size and architecture (Transformers) in discovering explanations for
novel phenomena.

该论文介绍了一种基于符号序列及其解释的机器学习框架 —— 解释性学习，提出了该领域的学习方法，并引入了一个演示环境（Odeen），在其中比较了基于解释性学习与传统机器学习范式的方法（如 Transformer）在发现新现象的解释方面的性能表现，发现基于解释性学习的 CRN 优于传统的方法。