International Journal of Approximate Reasoning最新文献_第3页

Three-way conflict analysis: From single-level to multi-level preferences 三向冲突分析：从单一层次偏好到多层次偏好

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.ijar.2025.109621

Guangming Lang , Haojun Liu , Mengjun Hu

In three-way conflict analysis, a key challenge lies in faithfully capturing agents’ attitudes towards multiple issues within complex conflict situations. Preference-based conflict situations, which characterize comparisons through ordered issue pairs, offer a structured alternative to traditional rating scales by emphasizing relational judgments. However, existing single-level preference frameworks are limited in their ability to capture variations in preference strengths, confidence levels across agents, and refinements that emerge over time. Moreover, they do not reliably support cross-agent comparison of preference relations. Consequently, single-level models exhibit inherent constraints when representing diverse agent viewpoints across different issue pairs. To overcome these limitations, this paper introduces a multi-level preference framework that generalizes single-level preference, converse, and indifference relations by incorporating multiple levels of relational intensity, thereby enabling a more fine-grained characterization of agents’ preference strengths over issue pairs. Within this framework, we define conflict measures for individual issue pairs between two agents, and further extend them to a set of issues, facilitating the exact quantification of conflict degrees between two agents across multiple issues, and enabling a more accurate trisection of agent pairs into alliance, neutrality, and conflict relations. As a concrete instantiation, we develop a two-level preference-based model distinguishing strong and weak relations, and apply it to a case study on development planning in Gansu Province. A comparative analysis demonstrates that the multi-level preference framework not only captures conflicts with greater expressiveness and accuracy than single-level approaches but also yields richer and more actionable insights for conflict resolution, thereby enhancing both the interpretability and the practical value of three-way conflict analysis.

在三方冲突分析中，一个关键的挑战在于忠实地捕捉agent对复杂冲突情境中多个问题的态度。基于偏好的冲突情境通过有序的问题对进行比较，通过强调关系判断，为传统评级量表提供了一种结构化的替代方案。然而，现有的单级偏好框架在捕捉偏好强度变化、跨代理的置信度以及随着时间的推移而出现的改进方面受到限制。此外，它们不能可靠地支持偏好关系的跨代理比较。因此，单级模型在表示跨不同问题对的不同代理观点时表现出固有的约束。为了克服这些限制，本文引入了一个多层次偏好框架，该框架通过结合多个层次的关系强度来概括单级偏好、反向和无差异关系，从而能够更细粒度地表征代理对问题对的偏好强度。在此框架内，我们定义了两个主体之间单个问题对的冲突度量，并进一步将其扩展到一组问题，从而便于准确量化两个主体之间跨多个问题的冲突程度，并能够更准确地将代理对划分为联盟关系、中立关系和冲突关系。作为具体实例，本文建立了基于偏好的强弱关系两级区分模型，并将其应用于甘肃省发展规划的案例研究。对比分析表明，多层次偏好框架不仅比单层次方法更能表达和准确地捕捉冲突，而且为解决冲突提供了更丰富和更具可操作性的见解，从而提高了三方冲突分析的可解释性和实用价值。

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引用次数: 0

Neural approaches to SAT solving: Design choices and interpretability 解决SAT的神经方法：设计选择和可解释性

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-03-01 Epub Date: 2025-12-05 DOI: 10.1016/j.ijar.2025.109609

David Mojžíšek , Jan Hůla , Ziwei Li , Ziyu Zhou , Mikoláš Janota

In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mechanisms enabling the model to generalize to different instances. We introduce several training improvements, particularly a novel closest assignment supervision method that dynamically adapts to the model’s current state, significantly enhancing performance on problems with larger solution spaces. Our experiments demonstrate the suitability of variable-clause graph representations with recurrent neural network updates, which achieve good accuracy on SAT assignment prediction while reducing computational demands. We extend the base graph neural network into a diffusion model that facilitates incremental sampling and can be effectively combined with classical techniques like unit propagation. Through analysis of embedding space patterns and optimization trajectories, we show how these networks implicitly perform a process very similar to continuous relaxations of MaxSAT, offering an interpretable view of their reasoning process. This understanding guides our design choices and explains the ability of recurrent architectures to scale effectively at inference time beyond their training distribution, which we demonstrate with test-time scaling experiments.

在这篇文章中，我们提供了应用于布尔可满足性问题的图神经网络的综合评估，并伴随着对使模型能够推广到不同实例的机制的直观解释。我们引入了一些训练改进，特别是一种新的最接近分配监督方法，它可以动态地适应模型的当前状态，显著提高了在具有较大解空间的问题上的性能。我们的实验证明了变量子句图表示与递归神经网络更新的适用性，在减少计算需求的同时，在SAT分配预测上取得了良好的准确性。我们将基图神经网络扩展为一个扩散模型，该模型便于增量采样，并且可以有效地与单元传播等经典技术相结合。通过对嵌入空间模式和优化轨迹的分析，我们展示了这些网络如何隐式地执行与MaxSAT连续松弛非常相似的过程，并提供了其推理过程的可解释视图。这种理解指导了我们的设计选择，并解释了循环架构在超出其训练分布的推理时间有效扩展的能力，我们通过测试时间扩展实验证明了这一点。

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引用次数: 0

Weighted feature graph-based multilabel feature selection via multi-metrics with global–local correlation 基于全局-局部相关的多指标加权特征图的多标签特征选择

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.ijar.2025.109623

Lin Sun , Changwu Feng , Xiankun Zhang , Jiucheng Xu

Due to the increasing prevalence of multilabel data, multilabel classification must tackle the challenges of high-dimensional feature spaces, complex label dependencies, and sample sparsity, which restricts the effectiveness of multilabel learning. To address these challenges, this work constructs a weighted feature graph-based multilabel feature selection methodology via multi-metrics with global–local correlation. First, fuzzy similarity relations in feature space and fuzzy decision similarity within label space are calculated. A joint fuzzy similarity relation is then constructed to capture the consistency of samples across both spaces. Next, we derive associativity from the joint fuzzy similarity relation, obtain redundancy from the fuzzy similarity relation, and measure interactivity using the fuzzy dependency degree of feature subsets. These three metrics are combined to define the edge weights of the feature graph, thereby describing the complex correlations among features for multilabel classification. Second, to evaluate the global–local correlation, a ridge regression coefficient matrix is presented. Concurrently, the correlation weight of information energy ratio is calculated via mutual information between features and labels, forming a feature-label correlation matrix. We then use a multi-Criteria Decision-Making (MCDM) framework to balance these global and local correlations. This allows us to develop a weighted feature-label matrix and a relative closeness degree for each alternative, which determines the node weights of the weighted feature graph. Third, to dynamically adjust the intensity of information propagation between features, a feature-driven, attention-based weight allocation strategy is studied. We construct a feature node matrix that represents multi-source information fusion and an attention matrix by combining graph structural features with the MCDM results. These are used to form a feature-aware hybrid adjacency matrix. An improved PageRank scheme then iteratively updates the feature ranking scores on this hybrid structure, to select a discriminative and representative feature subset. Experiments illustrate that our methodology outperforms other comparative approaches across several metrics on high-dimensional multilabel data.

由于多标签数据的日益普及，多标签分类必须解决高维特征空间、复杂标签依赖和样本稀疏性等问题，这些问题限制了多标签学习的有效性。为了解决这些问题，本文构建了一种基于加权特征图的多标签特征选择方法，该方法采用全局-局部相关的多指标。首先，计算特征空间中的模糊相似关系和标签空间中的模糊决策相似关系。然后构造一个联合模糊相似关系来捕获两个空间中样本的一致性。其次，从联合模糊相似关系中导出关联性，从模糊相似关系中获得冗余，并利用特征子集的模糊依赖度度量交互性。将这三个指标结合起来定义特征图的边缘权重，从而描述特征之间的复杂关联，用于多标签分类。其次，为了评估全局与局部的相关性，提出了脊回归系数矩阵。同时，通过特征与标签之间的互信息计算信息能量比的相关权重，形成特征-标签相关矩阵。然后，我们使用多标准决策（MCDM）框架来平衡这些全局和局部相关性。这允许我们开发一个加权特征标签矩阵和每个选项的相对接近度，这决定了加权特征图的节点权重。第三，为了动态调整特征之间的信息传播强度，研究了一种特征驱动、基于注意力的权重分配策略。将图结构特征与MCDM结果相结合，构造了表示多源信息融合的特征节点矩阵和注意矩阵。这些被用来形成一个特征感知的混合邻接矩阵。然后，改进的PageRank方案迭代更新该混合结构上的特征排名分数，以选择具有判别性和代表性的特征子集。实验表明，我们的方法在高维多标签数据的几个指标上优于其他比较方法。

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引用次数: 0

Data speak but sometimes lie: A game-theoretic approach to data bias and algorithmic fairness 数据说话，但有时撒谎：数据偏见和算法公平性的博弈论方法

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-03-01 Epub Date: 2025-12-06 DOI: 10.1016/j.ijar.2025.109608

Chiara Manganini, Esther Anna Corsi, Giuseppe Primiero

In the present work, we develop a novel information-theoretic and logic-based approach to data bias in Machine Learning predictions and show its relevance in the specific context of fairness evaluation. We frame predictions made on biased data as Ulam games, which formalise key aspects of data-driven inference, and from which a variation of the rational non-monotonic consequence relation can be defined. We investigate this framework to model how differential levels of noise in input features impact Machine Learning predictions. To the best of our knowledge, this is the first game-theoretic formalisation of ML unfairness.

在目前的工作中，我们开发了一种新的基于信息理论和逻辑的方法来处理机器学习预测中的数据偏差，并展示了其在公平评估的特定背景下的相关性。我们将对有偏差数据的预测框架为Ulam博弈，它形式化了数据驱动推理的关键方面，并从中定义了理性非单调结果关系的变化。我们研究了这个框架，以模拟输入特征中的不同噪声水平如何影响机器学习预测。据我们所知，这是ML不公平的第一个博弈论形式化。

引用次数: 0

How artificial intelligence leads to knowledge why: An inquiry inspired by Aristotle’s Posterior Analytics 人工智能如何导致知识为什么：受亚里士多德后验分析启发的探究

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1016/j.ijar.2025.109603

Guus Eelink , Kilian Rückschloß , Felix Weitkämper

Bayesian networks and causal models provide frameworks for reasoning about external interventions, enabling tasks that go beyond what probability distributions alone can support. Although these formalisms are often informally described as encoding causal knowledge, there is a lack of a formal theory that characterizes the kind of knowledge required to predict the effects of such interventions. This work introduces the theoretical framework of causal systems to implement Aristotle’s distinction between knowledge-that and knowledge-why within the setting of artificial intelligence. By interpreting existing AI technologies as causal systems, it examines the corresponding forms of knowledge they embody. Finally, it argues that predicting the effects of external interventions is possible only with knowledge-why, offering a more precise account of the assumptions underlying this capacity.

贝叶斯网络和因果模型为外部干预提供了推理框架，使任务超越了概率分布所能支持的范围。虽然这些形式主义经常被非正式地描述为编码因果知识，但缺乏一种正式的理论来描述预测此类干预措施的效果所需的知识。这项工作介绍了因果系统的理论框架，以实现亚里士多德在人工智能背景下对知识-那和知识-为什么的区分。通过将现有的人工智能技术解释为因果系统，它检查了它们所体现的相应形式的知识。最后，它认为预测外部干预的影响只有在“为什么”的知识下才有可能，并对这种能力背后的假设提供了更精确的解释。

引用次数: 0

Connecting classical finite exchangeability to quantum theory and indistinguishability 将经典的有限互换性与量子理论和不可区分性联系起来

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.ijar.2025.109604

Alessio Benavoli , Alessandro Facchini , Marco Zaffalon

Exchangeability is a fundamental concept in probability theory and statistics. It allows to model situations where the order of observations does not matter. The classical de Finetti’s theorem provides a representation of infinitely exchangeable sequences of random variables as mixtures of independent and identically distributed variables. The quantum de Finetti theorem extends this result to symmetric quantum states on tensor product Hilbert spaces. It is well known that both theorems do not hold for finitely exchangeable sequences. The aim of this work is to investigate two lesser-known representation theorems, which were developed in classical probability theory to extend de Finetti’s theorem to finitely exchangeable sequences by using quasi-probabilities and quasi-expectations. With the aid of these theorems, we illustrate how a de Finetti-like representation theorem for finitely exchangeable sequences can be formulated through a mathematical representation which is formally equivalent to quantum theory (with boson-symmetric density matrices). We then show a promising application of this connection to the challenge of defining entanglement for indistinguishable bosons.

互换性是概率论和统计学中的一个基本概念。它允许对观察顺序无关紧要的情况进行建模。经典的de Finetti定理将随机变量的无限交换序列表示为独立的和同分布的变量的混合物。量子德菲内蒂定理将这一结果推广到张量积希尔伯特空间上的对称量子态。众所周知，对于有限可交换序列，这两个定理并不成立。本文的目的是研究两个不太为人所知的表示定理，它们是在经典概率论中发展起来的，通过准概率和准期望将de Finetti定理扩展到有限交换序列。借助这些定理，我们说明了有限可交换序列的类德菲内蒂表示定理是如何通过形式上等价于量子理论（具有玻色子对称密度矩阵）的数学表示来表述的。然后，我们展示了这种联系在定义不可区分玻色子纠缠的挑战中的一个有希望的应用。

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引用次数: 0

Multi-granularity Knowledge Fusion for Feature Selection Using Granular-ball Entropy Uncertainty Measures 基于颗粒球熵不确定性测度的特征选择多粒度知识融合

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-02-01 Epub Date: 2025-11-11 DOI: 10.1016/j.ijar.2025.109590

Kehua Yuan , Yuji Bai , Duoqian Miao , Weiping Ding , Yiyu Yao , Hongyun Zhang , Witold Pedrycz

Multi-granularity computing for knowledge discovery has emerged as a remarkable paradigm in data mining and machine learning. As a representative method, granular-ball computing has attracted considerable attention due to its efficiency and adaptability in handling complex data distributions. However, most existing granularity-based approaches focus on intra-granular mutual information while neglecting the heterogeneity and overlapping phenomena across granularities. This limitation often leads to imprecise knowledge space construction and inaccurate uncertainty estimation in feature evaluation. To overcome this problem, this study proposes a novel and high-efficiency multi-granularity knowledge fusion framework for feature selection, incorporating an enhanced granular-ball generation mechanism and a newly designed granular-ball entropy (GB-E) uncertainty measure. Specifically, we first develop an enhanced granular-ball generation mechanism to construct multi-granularity knowledge space by incorporating class distribution information, thus achieving more accurate and flexible data partitioning. Subsequently, by jointly analyzing the separation and aggregation among granular balls, a novel granular-ball entropy is proposed to quantify uncertainty in the multi-granularity knowledge space. Compared with existing uncertainty measure methods, it provides a dual-perspective uncertainty characterization and effectively improves the accuracy of granularity information fusion. Furthermore, two feature significance measures based on the proposed GB-E measure are introduced for feature evaluation, and then a corresponding feature selection method is developed. Extensive experiments on multiple public datasets demonstrate the proposed method’s superior classification performance compared with several state-of-the-art approaches.

面向知识发现的多粒度计算已经成为数据挖掘和机器学习领域的一个重要范例。作为一种代表性的方法，颗粒球计算因其处理复杂数据分布的效率和适应性而受到广泛关注。然而，现有的基于粒度的方法大多侧重于粒度内的互信息，而忽略了粒度间的异质性和重叠现象。这种局限性往往导致特征评价中知识空间的构建不精确，不确定性估计不准确。为了克服这一问题，本研究提出了一种新的、高效的多粒度知识融合框架用于特征选择，该框架结合了增强的颗粒球生成机制和新设计的颗粒球熵（GB-E）不确定性测度。具体而言，我们首先开发了一种增强的颗粒球生成机制，通过结合类分布信息构建多粒度知识空间，从而实现更准确、更灵活的数据分区。随后，通过对颗粒球之间的分离和聚集进行分析，提出了一种新的颗粒球熵来量化多粒度知识空间中的不确定性。与现有的不确定性度量方法相比，该方法提供了双视角的不确定性表征，有效提高了粒度信息融合的精度。在此基础上，引入了基于GB-E测度的两种特征显著性测度进行特征评价，并提出了相应的特征选择方法。在多个公共数据集上进行的大量实验表明，与几种最先进的方法相比，该方法具有优越的分类性能。

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引用次数: 0

Machine learning for quantifier selection in cvc5 cvc5中量词选择的机器学习

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-02-01 Epub Date: 2025-11-15 DOI: 10.1016/j.ijar.2025.109602

Jan Jakubův , Mikoláš Janota , Jelle Piepenbrock , Josef Urban

In this work we considerably improve the real-time performance of state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an efficient machine learning model that informs the solver which quantifiers should be instantiated and which not. Each quantifier may be instantiated multiple times and the set of the currently active quantifiers changes as the solving progresses. Therefore, we invoke the ML predictor many times, during the whole run of the solver. To make this efficient, we use fast ML models based on gradient boosted decision trees. We integrate our approach into the state-of-the-art cvc5 SMT solver and show a considerable increase of the system’s holdout-set performance after training it on large sets of first-order problems. The method is tested in several ways, using both single-strategy and portfolio approaches. The evaluation is done on two large formal verification corpora: first-order problems created from the Mizar Mathematical Library, and first-order problems created from the HOL4 standard library.

在这项工作中，我们通过有效的机器学习指导量词选择，大大提高了最先进的SMT解决一阶量化问题的实时性。量词代表了SMT的一个重大挑战，并且在技术上是不可确定的来源。在我们的方法中，我们训练了一个有效的机器学习模型，该模型通知求解器哪些量词应该实例化，哪些不应该实例化。每个量词可以被实例化多次，并且当前活动量词的集合随着求解的进行而变化。因此，我们在求解器的整个运行过程中多次调用ML预测器。为了提高效率，我们使用基于梯度增强决策树的快速ML模型。我们将我们的方法集成到最先进的cvc5 SMT求解器中，并在对大的一阶问题集进行训练后，显示出系统的hold - out-set性能的显著提高。该方法经过了几种方法的测试，包括单一策略和投资组合方法。评估是在两个大型形式化验证语料库上完成的：从Mizar数学库创建的一阶问题，以及从HOL4标准库创建的一阶问题。

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引用次数: 0

A robust multi-source transfer classification method based on belief functions for cross-domain pattern recognition 基于信念函数的跨域模式识别鲁棒多源转移分类方法

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-02-01 Epub Date: 2025-10-30 DOI: 10.1016/j.ijar.2025.109589

Linqing Huang , Jinfu Fan , Gongshen Liu , Shilin Wang

In pattern recognition with few or no labeled data, domain adaptation techniques are often used to transfer knowledge in the source domain to help build classification models in the target domain. The effective combination of complementary information in multiple source domains usually can further improve the classification accuracy. To this end, we present a Robust Multi-Source Transfer (RMST) classification method consisting of two-step fusion of classifiers to extract the useful information in each source domain as much as possible, and to effectively combine the complementary information using belief functions in different source domains. The first step is to accurately predict the pseudo labels when computing conditional distribution, in order to learn a robust new feature representation, which is used to fuse diverse classifiers learnt by patterns for reliable soft classification in the second step of our algorithm. Furthermore, the soft classification results yielded by the assistance of different source domains are combined by belief functions with the new weighting factors taking into account the distribution discrepancy and classifier’s performance. The effectiveness of RMST was evaluated with respect to a variety of advanced methods, and the experimental results show that RMST can significantly improve the classification accuracy.

在很少或没有标记数据的模式识别中，通常使用领域自适应技术将源领域的知识转移到目标领域，以帮助构建目标领域的分类模型。多源域互补信息的有效组合通常可以进一步提高分类精度。为此，提出了一种两步融合分类器的鲁棒多源转移（RMST）分类方法，以尽可能多地提取每个源域的有用信息，并利用不同源域的信念函数有效地组合互补信息。第一步是在计算条件分布时准确预测伪标签，以学习一种鲁棒的新特征表示，用于融合由模式学习到的各种分类器进行可靠的软分类。此外，考虑分布差异和分类器性能，将不同源域辅助下的软分类结果与加权因子相结合。对比多种先进的分类方法对RMST的有效性进行了评价，实验结果表明RMST可以显著提高分类精度。

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引用次数: 0

ARIPOTER: Solvers for approximate reasoning based on grounded semantics ARIPOTER：基于接地语义的近似推理求解器

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2026-02-01 Epub Date: 2025-11-09 DOI: 10.1016/j.ijar.2025.109599

Jérôme Delobelle , Jean-Guy Mailly , Julien Rossit

Efficient computation of hard reasoning tasks is a key issue in abstract argumentation. One recent approach is to define approximate algorithms, i.e. methods that provide an answer that may not always be correct, but outperform the exact algorithms regarding the computation runtime. One such approach proposes to use the grounded semantics, which is polynomially computable, as a starting point for determining whether arguments are (credulously or skeptically) accepted with respect to various other extension-based semantics. In this paper, we push further this idea by defining a general family of approaches to evaluate the acceptability of arguments which are not in the grounded extension, neither attacked by it. These approaches rely on gradual semantics to evaluate these arguments. We also propose an approach using an heuristic based on the number of arguments attacked by or attacking an argument, and we show that this last approach, although seemingly different, is actually also an instance of our general family of approaches based on gradual semantics. We have implemented our approaches and provided an empirical study in which we discuss the results and compare our approach with the state-of-the-art approximate algorithms.

硬推理任务的高效计算是抽象论证中的一个关键问题。最近的一种方法是定义近似算法，即提供的答案可能并不总是正确的，但在计算运行时方面优于精确算法的方法。其中一种方法建议使用可多项式计算的基础语义作为起点，以确定相对于其他各种基于扩展的语义，参数是否（可信地或怀疑地）被接受。在本文中，我们通过定义一组一般的方法来进一步推动这一思想，以评估不属于基础扩展的论点的可接受性，也不受其攻击。这些方法依赖于渐进语义来评估这些参数。我们还提出了一种方法，使用启发式方法，基于被攻击或攻击一个论点的论点的数量，我们表明，最后一种方法，尽管看起来不同，实际上也是我们基于渐进语义的一般方法家族的一个实例。我们已经实现了我们的方法，并提供了一项实证研究，我们讨论了结果，并将我们的方法与最先进的近似算法进行了比较。

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引用次数: 0