A gradual semantics takes a weighted argumentation framework as input and
outputs a final acceptability degree for each argument, with different
semantics performing the computation in different manners. In this work, we
consider the problem of attack inference. That is, given a gradual semantics, a
set of arguments with associated initial weights, and the final desirable
acceptability degrees associated with each argument, we seek to determine
whether there is a set of attacks on those arguments such that we can obtain
these acceptability degrees. The main contribution of our work is to
demonstrate that the associated decision problem, i.e., whether a set of
attacks can exist which allows the final acceptability degrees to occur for
given initial weights, is NP-complete for the weighted h-categoriser and
cardinality-based semantics, and is polynomial for the weighted max-based
semantics, even for the complete version of the problem (where all initial
weights and final acceptability degrees are known). We then briefly discuss how
this decision problem can be modified to find the attacks themselves and
conclude by examining the partial problem where not all initial weights or
final acceptability degrees may be known.

本文研究了用加权论证框架实现逐步语义分析，并考虑了攻击推断问题。我们发现，在加权 h-categoriser 模型和 基于基数的模型中，判断攻击方案是否存在的决策问题是 NP 完全的，而在加权最大值模型中是多项式的。我们还研究了如何找到攻击方案，以及当初始权重或最终可接受度未知时的部分问题。

逐步语义的攻击关系推断

Inferring Attack Relations for Gradual Semantics

The topic of evaluating argument strength in various quantitative
argumentation systems has received increasing attention in the field of
abstract argumentation. However, the existing gradual semantics on argument
strength considers acceptability degree alone, which may be not sufficient to
evaluate arguments in practical scenarios. To adopt a richer characterization
for argument strength in real-world applications, we provide a novel
quantitative method called fuzzy labeling for fuzzy argumentation systems. For
fuzzy labeling, the argument strength is represented as a triple consisting of
acceptability, rejectability, and undecidability degree. With a richer scale,
it sheds new light on argument strength and gives us a deeper understanding
into status of arguments. For the purpose of evaluating arguments, we provide a
new way to establish gradual semantics by fuzzy labeling, which is crucial in
the evaluation process. We first investigate the rationality postulates of
fuzzy labeling, which are important for explaining the rationality of new
semantics taking into account the acceptability, rejectability and
undecidability degree together. We then propose a set of fuzzy labeling
semantics and prove some important properties which are crucial for comparing,
understanding and applying semantics.

本文提出了一种新的模糊标记量化法来评估实际场景中的争论强度，并探讨了其在渐进语义建立中的合理性和重要性。

模糊标记语义的定量论证

Fuzzy Labeling Semantics for Quantitative Argumentation

Gradual semantics within abstract argumentation associate a numeric score
with every argument in a system, which represents the level of acceptability of
this argument, and from which a preference ordering over arguments can be
derived. While some semantics operate over standard argumentation frameworks,
many utilise a weighted framework, where a numeric initial weight is associated
with each argument. Recent work has examined the inverse problem within gradual
semantics. Rather than determining a preference ordering given an argumentation
framework and a semantics, the inverse problem takes an argumentation
framework, a gradual semantics, and a preference ordering as inputs, and
identifies what weights are needed to over arguments in the framework to obtain
the desired preference ordering. Existing work has attacked the inverse problem
numerically, using a root finding algorithm (the bisection method) to identify
appropriate initial weights. In this paper we demonstrate that for a class of
gradual semantics, an analytical approach can be used to solve the inverse
problem. Unlike the current state-of-the-art, such an analytic approach can
rapidly find a solution, and is guaranteed to do so. In obtaining this result,
we are able to prove several important properties which previous work had posed
as conjectures.

本文讲述了针对逐渐语义中的反问题，使用解析方法而非当前数值方法来识别权重分配问题，从而使得问题可迅速寻找解决方案且具有保证性。同时，本文也证明了几个在前人猜测中重要的性质。

渐进语义内的反问题的解析解

Analytical Solutions for the Inverse Problem within Gradual Semantics

Gradual semantics with abstract argumentation provide each argument with a
score reflecting its acceptability, i.e. how "much" it is attacked by other
arguments. Many different gradual semantics have been proposed in the
literature, each following different principles and producing different
argument rankings. A sub-class of such semantics, the so-called weighted
semantics, takes, in addition to the graph structure, an initial set of weights
over the arguments as input, with these weights affecting the resultant
argument ranking. In this work, we consider the inverse problem over such
weighted semantics. That is, given an argumentation framework and a desired
argument ranking, we ask whether there exist initial weights such that a
particular semantics produces the given ranking. The contribution of this paper
are: (1) an algorithm to answer this problem, (2) a characterisation of the
properties that a gradual semantics must satisfy for the algorithm to operate,
and (3) an empirical evaluation of the proposed algorithm.

本文研究了加权阶段性语义，并提供了一个算法来解决加权初始义务以产生所需的论据排序这一问题，并给出了所需的阶段性语义的属性以使算法操作，同时进行了实证评估。