Recent work on LatPlan has shown that it is possible to learn models for
domain-independent classical planners from unlabeled image data. Although PDDL
models acquired by LatPlan can be solved using standard PDDL planners, the
resulting latent-space plan may be invalid with respect to the underlying,
ground-truth domain (e.g., the latent-space plan may include
hallucinatory/invalid states). We propose Plausibility-Based Heuristics, which
are domain-independent plausibility metrics which can be computed for each
state evaluated during search and uses as a heuristic function for best-first
search. We show that PBH significantly increases the number of valid found
plans on image-based tile puzzle and Towers of Hanoi domains.

LatPlan 通过无标签图像数据学习领域无关的经典计划器模型，提出了基于可信性的启发式方法 PBH，可以显著提高图像拼图和汉诺塔领域的有效解决方案数量。

基于可信度的潜在空间经典规划启发式

Plausibility-Based Heuristics for Latent Space Classical Planning

Recently, there is an increasing interest in obtaining the relational
structures of the environment in the Reinforcement Learning community. However,
the resulting "relations" are not the discrete, logical predicates compatible
to the symbolic reasoning such as classical planning or goal recognition.
Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between
deep-learning perceptual systems and symbolic classical planners. One key
component of the system is a Neural Network called State AutoEncoder (SAE),
which encodes an image-based input into a propositional representation
compatible to classical planning. To get the best of both worlds, we propose
First-Order State AutoEncoder, an unsupervised architecture for grounding the
first-order logic predicates and facts. Each predicate models a relationship
between objects by taking the interpretable arguments and returning a
propositional value. In the experiment using 8-Puzzle and a photo-realistic
Blocksworld environment, we show that (1) the resulting predicates capture the
interpretable relations (e.g. spatial), (2) they help obtaining the compact,
abstract model of the environment, and finally, (3) the resulting model is
compatible to symbolic classical planning.

提出了一种无监督的体系结构来命名一阶逻辑谓词和事实，以便将图像输入编码为符号计划兼容的命题表示，通过试验表明，所得到的谓词捕捉到可解释的关系，有助于获取环境的抽象模型，并且与符号计划兼容。

无监督图像为基础的可规划一阶逻辑表示

Unsupervised Grounding of Plannable First-Order Logic Representation  from Images

Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose LatPlan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), and a pair of images representing the
initial and the goal states (planning inputs), LatPlan finds a plan to the goal
state in a symbolic latent space and returns a visualized plan execution. The
contribution of this paper is twofold: (1) State Autoencoder, which finds a
propositional state representation of the environment using a Variational
Autoencoder. It generates a discrete latent vector from the images, based on
which a PDDL model can be constructed and then solved by an off-the-shelf
planner. (2) Action Autoencoder / Discriminator, a neural architecture which
jointly finds the action symbols and the implicit action models
(preconditions/effects), and provides a successor function for the implicit
graph search. We evaluate LatPlan using image-based versions of 3 planning
domains: 8-puzzle, Towers of Hanoi and LightsOut.

本文介绍了一种使用深度学习与经典规划相结合的无监督架构 LatPlan，通过提供一组图像对作为训练输入和一对初始和目标状态图像作为规划输入，可以使规划过程在符号潜在空间中找到计划并返回可视化的计划执行。