This paper presents a rich knowledge representation language aimed at
formalizing causal knowledge. This language is used for accurately and directly
formalizing common benchmark examples from the literature of actual causality.
A definition of cause is presented and used to analyze the actual causes of
changes with respect to sequences of actions representing those examples.

本文介绍了一种丰富的知识表示语言，旨在形式化因果知识，并使用该语言准确直接地形式化实际因果关系的常见基准例子。文章还提出了一个原因的定义，并用于分析代表这些例子的行动序列所引起的变化的实际原因。

利用答案集编程方法分析实际因果关系时融入背景知识

Embracing Background Knowledge in the Analysis of Actual Causality: An  Answer Set Programming Approach

Existential rules are an expressive knowledge representation language mainly
developed to query data. In the literature, they are often supposed to be in
some normal form that simplifies technical developments. For instance, a common
assumption is that rule heads are atomic, i.e., restricted to a single atom.
Such assumptions are considered to be made without loss of generality as long
as all sets of rules can be normalised while preserving entailment. However, an
important question is whether the properties that ensure the decidability of
reasoning are preserved as well. We provide a systematic study of the impact of
these procedures on the different chase variants with respect to chase
(non-)termination and FO-rewritability. This also leads us to study open
problems related to chase termination of independent interest.

该研究系统研究了存在性规则对于不同的追踪变体以及基于一阶逻辑的重写的 (chase (non-) termination and FO-rewritability) 影响，从而探讨了相关的开放性问题。

存在规则的规范化：不是那么无害！

Normalisations of Existential Rules: Not so Innocuous!

The paper presents a knowledge representation language $\mathcal{A}log$ which
extends ASP with aggregates. The goal is to have a language based on simple
syntax and clear intuitive and mathematical semantics. We give some properties
of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison
with other approaches.

该论文介绍了一种基于 ASP 和聚合的知识表示语言 Alog，旨在提供简单的语法和直观数学语义，包括其一些属性、计算答案集的算法和与其他方法的比较。

恶性循环原理和具有聚合功能的逻辑程序

Vicious Circle Principle and Logic Programs with Aggregates

The paper proposes a new knowledge representation language, called DLP<,
which extends disjunctive logic programming (with strong negation) by
inheritance. The addition of inheritance enhances the knowledge modeling
features of the language providing a natural representation of default
reasoning with exceptions.
A declarative model-theoretic semantics of DLP< is provided, which is shown
to generalize the Answer Set Semantics of disjunctive logic programs.
The knowledge modeling features of the language are illustrated by encoding
classical nonmonotonic problems in DLP<.
The complexity of DLP< is analyzed, proving that inheritance does not cause
any computational overhead, as reasoning in DLP< has exactly the same
complexity as reasoning in disjunctive logic programming. This is confirmed by
the existence of an efficient translation from DLP< to plain disjunctive logic
programming. Using this translation, an advanced KR system supporting the DLP<
language has been implemented on top of the DLV system and has subsequently
been integrated into DLV.

该论文提出了一种新的知识表示语言 DLP <，通过继承扩展了具有强否定的分段逻辑编程。文中提供了 DLP < 的声明性模型论语义，并通过将经典的非单调问题编码为 DLP < 来说明其知识建模功能，证明了继承不会导致任何计算开销。