Humans are capable of abstracting away irrelevant details when studying
problems. This is especially noticeable for problems over grid-cells, as humans
are able to disregard certain parts of the grid and focus on the key elements
important for the problem. Recently, the notion of abstraction has been
introduced for Answer Set Programming (ASP), a knowledge representation and
reasoning paradigm widely used in problem solving, with the potential to
understand the key elements of a program that play a role in finding a
solution. The present paper takes this further and empowers abstraction to deal
with structural aspects, and in particular with hierarchical abstraction over
the domain. We focus on obtaining the reasons for unsolvability of problems on
grids, and show the possibility to automatically achieve human-like
abstractions that distinguish only the relevant part of the grid. A user study
on abstract explanations confirms the similarity of the focus points in machine
vs. human explanations and reaffirms the challenge of employing abstraction to
obtain machine explanations.

本文介绍了如何使用抽象方法解决在网格上的问题，使用 Answer Set Programming（ASP）等解决问题的知识表达和推理范式，削减了网格的冗余信息。我们重点研究了网格问题的不可解原因，并通过用户研究验证了机器和人类结果的相似性以及抽象方法的挑战。

网格单元问题无解原因的缩放抽象

Abstraction for Zooming-In to Unsolvability Reasons of Grid-Cell  Problems

Current measures for evaluating text simplification systems focus on
evaluating lexical text aspects, neglecting its structural aspects. In this
paper we propose the first measure to address structural aspects of text
simplification, called SAMSA. It leverages recent advances in semantic parsing
to assess simplification quality by decomposing the input based on its semantic
structure and comparing it to the output. SAMSA provides a reference-less
automatic evaluation procedure, avoiding the problems that reference-based
methods face due to the vast space of valid simplifications for a given
sentence. Our human evaluation experiments show both SAMSA's substantial
correlation with human judgments, as well as the deficiency of existing
reference-based measures in evaluating structural simplification.

本文提出了 SAMSA 方法，用于评估文本简化中的结构方面，该方法利用了语义解析的最新进展，通过基于其语义结构分解输入并将其与输出进行比较来评估简化质量。SAMSA 提供了无参考自动评估程序，避免了基于参考的方法在给定句子的有效简化空间时所面临的问题。我们的人类评估实验证明 SAMSA 与人类判断具有显著相关性，并且现有基于参考的措施在评估结构简化方面存在不足。