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。

从状态空间结构学习规划的一阶符号表示

Learning First-Order Symbolic Representations for Planning from the  Structure of the State Space

During the 60s and 70s, AI researchers explored intuitions about intelligence
by writing programs that displayed intelligent behavior. Many good ideas came
out from this work but programs written by hand were not robust or general.
After the 80s, research increasingly shifted to the development of learners
capable of inferring behavior and functions from experience and data, and
solvers capable of tackling well-defined but intractable models like SAT,
classical planning, Bayesian networks, and POMDPs. The learning approach has
achieved considerable success but results in black boxes that do not have the
flexibility, transparency, and generality of their model-based counterparts.
Model-based approaches, on the other hand, require models and scalable
algorithms. Model-free learners and model-based solvers have close parallels
with Systems 1 and 2 in current theories of the human mind: the first, a fast,
opaque, and inflexible intuitive mind; the second, a slow, transparent, and
flexible analytical mind. In this paper, I review developments in AI and draw
on these theories to discuss the gap between model-free learners and
model-based solvers, a gap that needs to be bridged in order to have
intelligent systems that are robust and general.

该研究回顾了人工智能研究的进展，特别是在模型自由学习和模型基础求解器之间的差距，并讨论了如何弥合这一差距以获得具有健壮性和普适性的智能系统。