International Journal of Approximate Reasoning最新文献

英文中文

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 : 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

Feature enhancement-based network for few-shot image classification 基于特征增强的少拍图像分类网络

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

International Journal of Approximate Reasoning

Pub Date : 2025-11-19 DOI: 10.1016/j.ijar.2025.109605

Junbo Zhao , Lixin Han , Hong Yan

Few-shot image classification tackles recognizing new classes with limited training data. Despite the progress made by researchers in recent years, there are still challenges in dealing with data scarcity and improving model robustness. This paper presents FEICNet, a framework combining feature enhancement and adaptive weight control. Key innovations include: (1) MiniUNet (MUNet) — a compact network that enhances spatial and channel features through layered processing, and (2)Distribution of Feature Weights (DFW) – an adaptive weighting system that boosts critical features while filtering irrelevant patterns. Compatible with existing models without structural changes, the framework achieves strong performance across five benchmarks (MiniImageNet, tieredImageNet, StandfordDogs, CUB, StanfordCars). On StanfordCars, FEICNet outperforms Conv-64F methods by 13.4 % (1-shot) and 13.5 % (5-shot), surpassing ResNet-12 models by 9.7 % in 5-shot tests. Notably, when integrated as an embedding module, FEICNet elevates ProtoNets and Relation Networks by 19.6 %–28.8 % on StanfordDogs, demonstrating its effective plug-and-play capability. The framework exhibits consistent convergence across episodic training tasks, further evidencing its robustness. These advancements establish FEICNet as a significant contribution to few-shot image classification, particularly in resource-constrained environments and coarse- and fine-grained recognition scenarios. Overall, FEICNet provides a unified framework that bridges feature enhancement with adaptive weighting, offering new insights into few-shot image representation learning.

少拍图像分类解决了用有限的训练数据识别新类别的问题。尽管近年来研究人员取得了一些进展，但在处理数据稀缺性和提高模型鲁棒性方面仍然存在挑战。本文提出了一种结合特征增强和自适应权值控制的FEICNet框架。关键的创新包括：(1)MiniUNet （MUNet）——一个紧凑的网络，通过分层处理增强空间和通道特征；(2)特征权重分布（DFW）——一个自适应加权系统，在过滤无关模式的同时增强关键特征。该框架在不改变结构的情况下与现有模型兼容，在五个基准测试（MiniImageNet, tieredImageNet, StandfordDogs， CUB, StanfordCars）中实现了强大的性能。在StanfordCars上，FEICNet的性能比con - 64f高出13.4%（1次射击）和13.5%（5次射击），在5次射击测试中比ResNet-12模型高出9.7%。值得注意的是，当作为嵌入模块集成时，FEICNet将ProtoNets和Relation Networks在StanfordDogs上提升了19.6% - 28.8%，证明了其有效的即插即用能力。该框架在情景训练任务中表现出一致的收敛性，进一步证明了其鲁棒性。这些进步使FEICNet成为对少量图像分类的重要贡献，特别是在资源受限环境和粗粒度和细粒度识别场景中。总的来说，FEICNet提供了一个统一的框架，将特征增强与自适应加权连接起来，为少量图像表示学习提供了新的见解。

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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 : 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

Disentangled representations for continuous treatment effect estimation 连续处理效果估计的解纠缠表示

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

International Journal of Approximate Reasoning

Pub Date : 2025-11-16 DOI: 10.1016/j.ijar.2025.109601

Ruijing Cui, Jianbin Sun, Bingyu He, Kewei Yang, Bingfeng Ge

We focus on estimating the causal effects of continuous treatments, also known as the dose-response function. Current methods typically learn a treatment-agnostic representation for all covariates, without distinguishing between instrumental, confounding, and adjustment variables among the covariates. Although some researchers disentangle covariates to estimate treatment effects, these methods are limited to the binary treatment setting and fail to obtain independent disentangled factors. So, learning the underlying disentangled factors precisely remains an open problem. In this paper, we incorporate the disentangled representation into the setting of continuous treatment and propose a novel model for dose-response curve estimation. Mutual information estimators and Integral Probability Metric distances effectively ensure the independence for disentangled factors. Extensive results on synthetic and semi-synthetic datasets demonstrate that our model outperforms current state-of-the-art methods.

我们的重点是估计连续治疗的因果效应，也被称为剂量-反应函数。目前的方法通常学习所有协变量的治疗不可知论表示，而不区分协变量中的工具变量、混杂变量和调整变量。尽管一些研究者通过解纠缠协变量来估计治疗效果，但这些方法仅限于二元治疗设置，无法获得独立的解纠缠因子。因此，准确地了解潜在的解开因素仍然是一个悬而未决的问题。在本文中，我们将解纠缠表示纳入到连续处理的设定中，并提出了一个新的剂量-响应曲线估计模型。互信息估计和积分概率度量距离有效地保证了解纠缠因子的独立性。在合成和半合成数据集上的广泛结果表明，我们的模型优于当前最先进的方法。

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 : 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

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 : 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

Matrix-based efficient methods to update three-way regions in neighborhood systems under varying attributes 基于矩阵的邻域系统三向区域更新方法

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

International Journal of Approximate Reasoning

Pub Date : 2025-11-10 DOI: 10.1016/j.ijar.2025.109598

Yingqi Qi , Chengxiang Hu , Xiaoling Huang

Traditional set-based methods for computing three-way regions in neighborhood systems primarily rely on the inclusion relationships between target concepts and neighborhood classes to process continuous numerical data. However, these methods exhibit significant limitations when applied to time-varying neighborhood information systems, as they inherently lack the capability to accommodate dynamically evolving data, effectively. To overcome this challenge, our research presents novel matrix-based incremental methods that leverage previously computed results to enable more efficient updating and maintenance of three-way regions in neighborhood rough sets. Through comprehensive integration and analysis of neighborhood information systems with a focus on varying attributes, we develop matrix-based incremental mechanisms. Building on these mechanisms, we propose two incremental algorithms to effectively handle dynamic numerical data. Experimental results demonstrate the effectiveness and superior efficiency of the proposed methods compared to existing approaches. Specifically, the proposed algorithms exhibit lower computational time and higher speed-up ratio, highlighting their efficiency for updating neighborhood three-way regions.

传统的基于集的邻域系统三向区域计算方法主要依靠目标概念和邻域类之间的包含关系来处理连续数值数据。然而，当应用于时变邻域信息系统时，这些方法表现出明显的局限性，因为它们固有地缺乏有效适应动态发展数据的能力。为了克服这一挑战，我们的研究提出了新的基于矩阵的增量方法，利用先前计算的结果来更有效地更新和维护邻域粗糙集中的三向区域。通过对不同属性邻域信息系统的综合集成和分析，提出了基于矩阵的增量机制。在这些机制的基础上，我们提出了两种增量算法来有效地处理动态数值数据。实验结果表明，与现有方法相比，所提方法具有较高的有效性和效率。具体而言，该算法具有较低的计算时间和较高的加速比，突出了其更新邻域三向区域的效率。

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引用次数: 0

Measuring external conflict in Dempster-Shafer theory based on Kantorovich problems 基于Kantorovich问题的Dempster-Shafer理论中的外部冲突度量

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

International Journal of Approximate Reasoning

Pub Date : 2025-11-09 DOI: 10.1016/j.ijar.2025.109597

Andrey G. Bronevich , Alexander E. Lepskiy

In the paper, we consider three possible types of external conflict in Dempster-Shafer theory and propose its measurement based on functionals evaluating intersection, inclusion and distance between random sets. All proposed functionals can be viewed as extensions of known functionals like Jaccard metric, Jaccard index, and Dice coefficient from usual sets to random sets based on the solutions of the Kantorovich problems.

在本文中，我们考虑了Dempster-Shafer理论中三种可能的外部冲突类型，并提出了基于评估随机集之间的交集、包含和距离的泛函度量方法。所有提出的泛函都可以看作是已知泛函的扩展，如Jaccard度量、Jaccard指数和Dice系数，从通常集合到基于Kantorovich问题解的随机集合。

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 : 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

On exact regions between measures of concordance and Chatterjee’s rank correlation for lower semilinear copulas 关于下半线性联结的一致性测度与查特吉等级相关之间的精确区域

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

International Journal of Approximate Reasoning

Pub Date : 2025-11-02 DOI: 10.1016/j.ijar.2025.109588

Sebastian Fuchs, Carsten Limbach, Fabian Schürrer

We explore how the classical concordance measures–Kendall’s

τ

, Spearman’s rank correlation

ρ

, and Spearman’s footrule

ϕ

–relate to Chatterjee’s rank correlation

ξ

when restricted to lower semilinear copulas. First, we provide a complete characterization of the attainable

τ - ρ

region for this class, thus resolving the conjecture of Maislinger and Trutschnig [1]. Building on this result, we then derive the exact

τ - ϕ

and

ϕ - ρ

regions, obtain a closed-form relationship between

ξ

and

τ

, and establish the exact

τ - ξ

region. In particular, we prove that

ξ

never exceeds

τ

ρ

, or

ϕ

. Our results clarify the relationship between undirected and directed dependence measures and reveal novel insights into the dependence structures that result from lower semilinear copulas.

我们探讨了经典的一致性测量- kendall的τ， Spearman的秩相关ρ和Spearman的阶导数 -在限制于较低的半线性联结时如何与Chatterjee的秩相关ξ相关。首先，我们给出了该类可得τ−ρ区域的完整表征，从而解决了Maislinger和Trutschnig的猜想。在此结果的基础上，我们推导出精确的τ−φ和φ−ρ区域，得到ξ和τ之间的封闭形式关系，并建立精确的τ−ξ区域。特别地，我们证明了ξ从不超过τ， ρ，或φ。我们的研究结果阐明了无向和有向依赖度量之间的关系，并揭示了对由低半线性联结引起的依赖结构的新见解。

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