One of the main obstacles for developing flexible AI systems is the split
between data-based learners and model-based solvers. Solvers such as classical
planners are very flexible and can deal with a variety of problem instances and
goals but require first-order symbolic models. Data-based learners, on the
other hand, are robust but do not produce such representations. In this work we
address this split by showing how the first-order symbolic representations that
are used by planners can be learned from non-symbolic inputs that encode the
structure of the state space. The representation learning problem is formulated
as the problem of inferring planning instances over a common but unknown
first-order domain that account for the structure of the observed state space.
This means to infer a complete first-order representation (i.e. general action
schemas, relational symbols, and objects) that explains the observed state
space structures. The inference problem is cast as a two-level combinatorial
search where the outer level searches for values of a small set of
hyperparameters and the inner level, solved via SAT, searches for a first-order
symbolic model. The framework is shown to produce general and correct
first-order representations for standard problems like Gripper, Blocksworld,
and Hanoi from input graphs that encode the flat state-space structure of a
single instance.

通过将规划器使用的一阶符号表示从编码状态空间结构的非符号输入中学习，我们解决了图像与符号之间的鸿沟，这意味着推断一个完整的一阶表示（即一般的动作模式、关系符号和对象）来解释所观察到的状态空间结构。