Artificial Intelligence最新文献

英文中文

Automated planning instance generation with neuro-symbolic AI 使用神经符号AI自动规划实例生成

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-28 DOI: 10.1016/j.artint.2025.104471

Carlos Núñez-Molina , Pablo Mesejo , Juan Fernández-Olivares

In the field of Automated Planning there is often the need for a set of planning problems from a particular domain, e.g., to be used as training data for Machine Learning methods or as benchmarks in planning competitions. In most cases, these problems are created either by hand or by a domain-specific generator, putting a burden on the human designers. In this paper, we propose NeSIG (Neuro-Symbolic Instance Generator), to the best of our knowledge the first domain-independent method for automatically generating typed-STRIPS planning problems that are valid, diverse and difficult to solve. We formulate problem generation as a Markov Decision Process and train two generative policies with Deep Reinforcement Learning to generate problems with the desired properties. We conduct experiments on five classical domains, comparing our approach against handcrafted, domain-specific instance generators and various ablations. Results show NeSIG is able to automatically generate valid and diverse problems of much greater difficulty (6.8 times more on geometric average) than domain-specific generators, while simultaneously reducing human effort when compared to them. Additionally, it can generalize to problems more than twice the size of those seen during training.

在自动化规划领域，通常需要一组来自特定领域的规划问题，例如，用作机器学习方法的训练数据或作为规划竞赛的基准。在大多数情况下，这些问题要么是手工创建的，要么是由特定于领域的生成器创建的，这给人类设计人员带来了负担。在本文中，我们提出了NeSIG（神经符号实例生成器），据我们所知，这是第一个独立于领域的方法，用于自动生成有效的、多样化的、难以解决的类型条带规划问题。我们将问题生成制定为马尔可夫决策过程，并使用深度强化学习训练两个生成策略来生成具有所需属性的问题。我们在五个经典领域进行了实验，将我们的方法与手工制作的、特定于领域的实例生成器和各种消融进行了比较。结果表明，NeSIG能够自动生成比特定领域生成器难度大得多的有效和多样化的问题（几何平均难度为6.8倍），同时减少了人工工作量。此外，它可以泛化到比训练中看到的问题大一倍以上的问题。

{"title":"Automated planning instance generation with neuro-symbolic AI","authors":"Carlos Núñez-Molina , Pablo Mesejo , Juan Fernández-Olivares","doi":"10.1016/j.artint.2025.104471","DOIUrl":"10.1016/j.artint.2025.104471","url":null,"abstract":"<div><div>In the field of Automated Planning there is often the need for a set of planning problems from a particular domain, e.g., to be used as training data for Machine Learning methods or as benchmarks in planning competitions. In most cases, these problems are created either by hand or by a domain-specific generator, putting a burden on the human designers. In this paper, we propose NeSIG (<em>Neuro-Symbolic Instance Generator</em>), to the best of our knowledge the first domain-independent method for automatically generating typed-STRIPS planning problems that are valid, diverse and difficult to solve. We formulate problem generation as a Markov Decision Process and train two generative policies with Deep Reinforcement Learning to generate problems with the desired properties. We conduct experiments on five classical domains, comparing our approach against handcrafted, domain-specific instance generators and various ablations. Results show NeSIG is able to automatically generate valid and diverse problems of much greater difficulty (6.8 times more on geometric average) than domain-specific generators, while simultaneously reducing human effort when compared to them. Additionally, it can generalize to problems more than twice the size of those seen during training.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"352 ","pages":"Article 104471"},"PeriodicalIF":4.6,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LAD2025, A constraint-based solver for the subgraph isomorphism problem 基于约束的子图同构问题求解器LAD2025

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-25 DOI: 10.1016/j.artint.2025.104474

Christine Solnon

The Subgraph Isomorphism Problem (SIP) is an

N P

-complete problem that aims at finding a copy of a pattern graph in a target graph. It may be modelled as a constraint satisfaction problem in a very straightforward way, and exact approaches for solving SIPs usually propagate constraints to reduce the search space. In particular, PathLAD is a solver introduced in 2016 that combines Locally All Different (LAD) constraints with path-based supplemental constraints. In this paper, we introduce LAD2025, which combines a complete refactoring of PathLAD with new features: new supplemental constraints, a weight-based variable ordering heuristic, random restarts with nogood recording, a new value ordering heuristic and a rule for selecting the level of filtering.

子图同构问题（SIP）是一个np完全问题，其目的是在目标图中找到一个模式图的副本。它可以以一种非常直接的方式建模为约束满足问题，并且解决sip的精确方法通常传播约束以减少搜索空间。特别是，PathLAD是2016年推出的一款求解器，它结合了局部所有不同（local All Different， LAD）约束和基于路径的补充约束。在本文中，我们介绍了LAD2025，它结合了PathLAD的完整重构和新的特征：新的补充约束，基于权重的变量排序启发式，无良好记录的随机重启，新的值排序启发式和选择过滤级别的规则。

引用次数: 0

A structural complexity analysis of synchronous dynamical systems 同步动力系统的结构复杂性分析

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-18 DOI: 10.1016/j.artint.2025.104472

Eduard Eiben , Robert Ganian , Thekla Hamm , Viktoriia Korchemna

Synchronous dynamical systems are well-established models that have been used to capture a range of phenomena in networks, including opinion diffusion, spread of disease and product adoption. We study the three most notable problems in synchronous dynamical systems: whether the system will transition to a target configuration from a starting configuration, whether the system will reach convergence from a starting configuration, and whether the system is guaranteed to converge from every possible starting configuration. While all three problems were known to be intractable in the classical sense, we initiate the study of their exact boundaries of tractability from the perspective of structural parameters of the network by making use of the more fine-grained parameterized complexity paradigm. As our first result, we consider treewidth—as the most prominent and ubiquitous structural parameter—and show that all three problems remain intractable even on instances of constant treewidth. We complement this negative finding with fixed-parameter algorithms for the former two problems parameterized by treedepth, a well-studied restriction of treewidth. While it is possible to rule out a similar algorithm for convergence guarantee under treedepth, we conclude with a fixed-parameter algorithm for this last problem when parameterized by treedepth and the maximum in-degree.

同步动力系统是一种成熟的模型，已被用于捕捉网络中的一系列现象，包括意见扩散、疾病传播和产品采用。研究了同步动力系统中三个最重要的问题：系统是否会从一个起始构型过渡到一个目标构型，系统是否会从一个起始构型达到收敛，以及系统是否保证从每一个可能的起始构型收敛。虽然已知这三个问题在经典意义上都是难以解决的，但我们通过使用更细粒度的参数化复杂性范式，从网络结构参数的角度开始研究它们的可追溯性的确切边界。作为我们的第一个结果，我们将树宽视为最突出和最普遍的结构参数，并表明即使在树宽不变的情况下，这三个问题仍然难以解决。对于前两个问题，我们用固定参数算法来补充这个否定的发现，固定参数算法是由树深参数化的，树宽是一个很好的研究限制。虽然在树深下可以排除类似的收敛保证算法，但当用树深和最大入度参数化时，我们得出最后一个问题的固定参数算法。

{"title":"A structural complexity analysis of synchronous dynamical systems","authors":"Eduard Eiben , Robert Ganian , Thekla Hamm , Viktoriia Korchemna","doi":"10.1016/j.artint.2025.104472","DOIUrl":"10.1016/j.artint.2025.104472","url":null,"abstract":"<div><div>Synchronous dynamical systems are well-established models that have been used to capture a range of phenomena in networks, including opinion diffusion, spread of disease and product adoption. We study the three most notable problems in synchronous dynamical systems: whether the system will transition to a target configuration from a starting configuration, whether the system will reach convergence from a starting configuration, and whether the system is guaranteed to converge from every possible starting configuration. While all three problems were known to be intractable in the classical sense, we initiate the study of their exact boundaries of tractability from the perspective of structural parameters of the network by making use of the more fine-grained parameterized complexity paradigm. As our first result, we consider treewidth—as the most prominent and ubiquitous structural parameter—and show that all three problems remain intractable even on instances of constant treewidth. We complement this negative finding with fixed-parameter algorithms for the former two problems parameterized by treedepth, a well-studied restriction of treewidth. While it is possible to rule out a similar algorithm for convergence guarantee under treedepth, we conclude with a fixed-parameter algorithm for this last problem when parameterized by treedepth and the maximum in-degree.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104472"},"PeriodicalIF":4.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using execution logs for improving Pseudo-Boolean propagation 使用执行日志改进伪布尔传播

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-18 DOI: 10.1016/j.artint.2025.104470

Robert Nieuwenhuis , Albert Oliveras , Enric Rodríguez-Carbonell , Rui Zhao

Among all procedures that CDCL-based SAT solvers implement, unit propagation dominates the total running time. Hence, it is not a surprise that large research efforts have been invested on improving it. As a result, the two-watched-literal scheme, enhanced with implementation details boosting its performance, emerged as the dominant method.

The importance of unit propagation in pseudo-Boolean solvers is similar. However, no dominant method exists: counter and watch-based propagation are well-suited for different types of constraints, opening the door to hybrid methods. The higher complexity of implementing pseudo-Boolean solvers has shifted the research focus to higher-level aspects of other procedures, considering implementation details of unit propagation not a priority.

In this paper, we first present execution logs: a novel methodology that allows us to precisely evaluate the performance of different propagation procedures. Secondly, we show how both counter and watch-based propagation routines in the RoundingSat solver can be largely improved thanks to a careful analysis of various implementation issues. Thirdly, a detailed analysis shows that hybrid methods outperform the ones based on a single technique. Finally, our experiments reveal that improvements in propagation lead to a clearly better overall performance of the solver.

在基于cdcl的SAT求解器实现的所有程序中，单元传播占总运行时间的主导地位。因此，投入大量的研究努力来改进它也就不足为奇了。结果，通过增强实现细节来提高性能的双监视文字方案成为了主要的方法。在伪布尔解算器中，单位传播的重要性是相似的。然而，不存在占主导地位的方法：计数器和基于监视的传播非常适合不同类型的约束，为混合方法打开了大门。实现伪布尔解算器的较高复杂性已经将研究重点转移到其他程序的更高级别方面，考虑单元传播的实现细节而不是优先考虑。在本文中，我们首先介绍了执行日志：一种允许我们精确评估不同传播过程性能的新方法。其次，通过对各种实现问题的仔细分析，我们展示了如何在很大程度上改进RoundingSat求解器中基于计数器和监视的传播例程。第三，详细分析表明，混合方法优于基于单一技术的方法。最后，我们的实验表明，传播的改进导致求解器的整体性能明显更好。

{"title":"Using execution logs for improving Pseudo-Boolean propagation","authors":"Robert Nieuwenhuis , Albert Oliveras , Enric Rodríguez-Carbonell , Rui Zhao","doi":"10.1016/j.artint.2025.104470","DOIUrl":"10.1016/j.artint.2025.104470","url":null,"abstract":"<div><div>Among all procedures that CDCL-based SAT solvers implement, unit propagation dominates the total running time. Hence, it is not a surprise that large research efforts have been invested on improving it. As a result, the two-watched-literal scheme, enhanced with implementation details boosting its performance, emerged as the dominant method.</div><div>The importance of unit propagation in pseudo-Boolean solvers is similar. However, no dominant method exists: counter and watch-based propagation are well-suited for different types of constraints, opening the door to hybrid methods. The higher complexity of implementing pseudo-Boolean solvers has shifted the research focus to higher-level aspects of other procedures, considering implementation details of unit propagation not a priority.</div><div>In this paper, we first present <em>execution logs:</em> a novel methodology that allows us to precisely evaluate the performance of different propagation procedures. Secondly, we show how both counter and watch-based propagation routines in the <span>RoundingSat</span> solver can be largely improved thanks to a careful analysis of various implementation issues. Thirdly, a detailed analysis shows that hybrid methods outperform the ones based on a single technique. Finally, our experiments reveal that improvements in propagation lead to a clearly better overall performance of the solver.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104470"},"PeriodicalIF":4.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistically robust counterfactual explanations under model changes 模型变化下的概率稳健反事实解释

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-08 DOI: 10.1016/j.artint.2025.104459

Luca Marzari , Francesco Leofante , Ferdinando Cicalese , Alessandro Farinelli

We study the problem of generating robust counterfactual explanations for deep learning models subject to model changes. We focus on plausible model changes altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for the first time that computing the robustness of counterfactuals with respect to model changes is NP-hard. As this (practically) rules out the existence of scalable algorithms for exactly computing robustness, we propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees while preserving scalability. Remarkably, and differently from existing solutions targeting plausible model changes, our approach does not impose requirements on the network to be analysed, thus enabling robustness analysis on a wider range of architectures, including state-of-the-art tabular transformers. A thorough experimental analysis on four binary classification datasets reveals that our method improves the state of the art in generating robust explanations, outperforming existing methods.

我们研究了为受模型变化影响的深度学习模型生成鲁棒反事实解释的问题。我们将重点放在改变模型参数的合理模型变化上，并提出了一个新的框架来解释这种情况下的鲁棒性。为了激励我们的解决方案，我们首先展示了计算关于模型变化的反事实的鲁棒性是np困难的。由于这（实际上）排除了精确计算鲁棒性的可扩展算法的存在，我们提出了一种新的概率方法，该方法能够在保持可扩展性的同时，提供具有强保证的严格鲁棒性估计。值得注意的是，与现有的针对合理模型变化的解决方案不同，我们的方法不会对要分析的网络施加要求，因此可以在更广泛的体系结构上进行鲁棒性分析，包括最先进的表格变压器。对四个二元分类数据集的彻底实验分析表明，我们的方法在生成鲁棒性解释方面提高了技术水平，优于现有方法。

{"title":"Probabilistically robust counterfactual explanations under model changes","authors":"Luca Marzari , Francesco Leofante , Ferdinando Cicalese , Alessandro Farinelli","doi":"10.1016/j.artint.2025.104459","DOIUrl":"10.1016/j.artint.2025.104459","url":null,"abstract":"<div><div>We study the problem of generating robust counterfactual explanations for deep learning models subject to model changes. We focus on <em>plausible model changes</em> altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for the first time that computing the robustness of counterfactuals with respect to model changes is NP-hard. As this (practically) rules out the existence of scalable algorithms for exactly computing robustness, we propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees while preserving scalability. Remarkably, and differently from existing solutions targeting plausible model changes, our approach does not impose requirements on the network to be analysed, thus enabling robustness analysis on a wider range of architectures, including state-of-the-art tabular transformers. A thorough experimental analysis on four binary classification datasets reveals that our method improves the state of the art in generating robust explanations, outperforming existing methods.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104459"},"PeriodicalIF":4.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is DIBBS a DXBB algorithm? DIBBS是DXBB算法吗？

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-08 DOI: 10.1016/j.artint.2025.104468

Nathan R. Sturtevant , Shahaf Shperberg , Ariel Felner

The recently-introduced Dynamically Improved Bounds Bidirectional Search (DIBBS) algorithm attributes its success to the fact that it is not a deterministic expansion-based black box algorithm (DXBB). After communication with the authors, there is agreement that this characterization is incorrect. The goal of this research note is to provide correction in the literature regarding the claims around DIBBS, to make it clearer why DIBBS is a DXBB algorithm, and to explain why its performance is bounded by bidirectional search theory.

最近引入的动态改进边界双向搜索（DIBBS）算法将其成功归因于它不是基于确定性展开的黑盒算法（DXBB）。在与作者沟通后，大家一致认为这种描述是不正确的。本研究报告的目的是纠正有关DIBBS的声明的文献，使DIBBS更清楚地说明为什么DIBBS是DXBB算法，并解释为什么它的性能受到双向搜索理论的限制。

引用次数: 0

Multi-objective reinforcement learning for provably incentivising alignment with value systems 多目标强化学习可证明激励与价值系统对齐

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-07 DOI: 10.1016/j.artint.2025.104460

Manel Rodriguez-Soto , Roxana Rădulescu , Filippo Bistaffa , Oriol Ricart , Arnau Mayoral-Macau , Maite Lopez-Sanchez , Juan A. Rodriguez-Aguilar , Ann Nowé

This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while striving to achieve its individual objective. To address this value alignment problem, we adopt the Multi-Objective Reinforcement Learning framework and propose a novel algorithm that combines techniques from Multi-Objective Reinforcement Learning and Linear Programming. In addition, we illustrate our value alignment process with an example involving an autonomous vehicle. Here, we demonstrate that the agent learns to behave in alignment with the ethical values of safety, achievement, and comfort, with achievement representing the agent’s individual objective. Such ethical behaviour differs depending on the ordering between values. We also use a synthetic multi-objective environment to evaluate the computational costs of guaranteeing ethical learning as the number of values increases.

本文解决了确保自主学习代理与多种道德价值观保持一致的问题。具体来说，我们提出了创建一个环境所需的理论原则和算法工具，在这个环境中，我们确保代理在努力实现其个人目标的同时学习与多种道德价值观相一致的行为。为了解决这个值对齐问题，我们采用了多目标强化学习框架，并提出了一种结合多目标强化学习和线性规划技术的新算法。此外，我们用一个涉及自动驾驶汽车的例子来说明我们的价值校准过程。在这里，我们证明了代理学习与安全、成就和舒适的道德价值观一致的行为，而成就代表了代理的个人目标。这种道德行为的不同取决于价值观之间的顺序。我们还使用一个合成的多目标环境来评估随着值的增加而保证伦理学习的计算成本。

{"title":"Multi-objective reinforcement learning for provably incentivising alignment with value systems","authors":"Manel Rodriguez-Soto , Roxana Rădulescu , Filippo Bistaffa , Oriol Ricart , Arnau Mayoral-Macau , Maite Lopez-Sanchez , Juan A. Rodriguez-Aguilar , Ann Nowé","doi":"10.1016/j.artint.2025.104460","DOIUrl":"10.1016/j.artint.2025.104460","url":null,"abstract":"<div><div>This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while striving to achieve its individual objective. To address this value alignment problem, we adopt the Multi-Objective Reinforcement Learning framework and propose a novel algorithm that combines techniques from Multi-Objective Reinforcement Learning and Linear Programming. In addition, we illustrate our value alignment process with an example involving an autonomous vehicle. Here, we demonstrate that the agent learns to behave in alignment with the ethical values of safety, achievement, and comfort, with achievement representing the agent’s individual objective. Such ethical behaviour differs depending on the ordering between values. We also use a synthetic multi-objective environment to evaluate the computational costs of guaranteeing ethical learning as the number of values increases.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104460"},"PeriodicalIF":4.6,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defining defense and defeat in abstract argumentation from scratch – A generalizing approach 在抽象论证中从零开始定义辩护和失败——一种一般化的方法

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-12-05 DOI: 10.1016/j.artint.2025.104456

Lydia Blümel , Markus Ulbricht

We propose a general framework to investigate semantics of Dung-style argumentation frameworks (AFs) by means of a generic defeat notion formalized by refute operators.The main idea underlying our approach is that, given a refute operator δ, counter-parts to all classical semantics can be deduced in a natural way. We demonstrate how classical as well as recent proposals can be captured by our approach when utilizing suitable refute operators. In addition, we showcase how our general scheme can be employed to propose novel semantics in a systematic manner. This results in what we call cooperative semantics which stem from a novel refute operator we will introduce. We perform an in-depth investigation of basic properties of refute operators and to which extent the induced semantics inherit desirable properties from it. Among others, we show under which conditions i) a counterpart to Dung’s fundamental lemma can be inferred, ii) the generalized version of the grounded extension is unique, or iii) the generalized version of stable semantics does not collapse. Moreover, we contribute to a principle-based study of AF semantics by discussing properties tailored to assess the behavior of different refute operators. This includes an investigation of means to compare refute operators in terms of their aggressiveness. Finally, we conclude the study by reporting computational complexity results for basic reasoning tasks which hold in our general framework.

我们提出了一个通用框架，通过一个由驳斥算子形式化的通用失败概念来研究Dung-style论证框架（AFs）的语义。我们方法的主要思想是，给定一个反驳算子δ，所有经典语义的对应部分都可以以自然的方式推导出来。我们演示了在使用合适的反驳操作符时，如何通过我们的方法捕获经典和最近的建议。此外，我们展示了如何使用我们的一般方案以系统的方式提出新的语义。这导致了我们所说的合作语义，它源于我们将介绍的一个新的驳斥算子。我们对反驳算子的基本性质进行了深入的研究，并在多大程度上从它继承了诱导语义的理想性质。其中，我们证明了在哪些条件下i) Dung的基本引理的对立物可以被推断，ii)基于扩展的广义版本是唯一的，或iii)稳定语义的广义版本不坍缩。此外，我们通过讨论为评估不同驳斥算子的行为而量身定制的属性，为基于原则的AF语义研究做出了贡献。这包括一项调查的手段，比较反驳运营商在其侵略性方面。最后，我们通过报告在我们的一般框架中持有的基本推理任务的计算复杂性结果来总结研究。

{"title":"Defining defense and defeat in abstract argumentation from scratch – A generalizing approach","authors":"Lydia Blümel , Markus Ulbricht","doi":"10.1016/j.artint.2025.104456","DOIUrl":"10.1016/j.artint.2025.104456","url":null,"abstract":"<div><div>We propose a general framework to investigate semantics of Dung-style argumentation frameworks (AFs) by means of a generic defeat notion formalized by <em>refute operators</em>.The main idea underlying our approach is that, given a refute operator <em>δ</em>, counter-parts to all classical semantics can be deduced in a natural way. We demonstrate how classical as well as recent proposals can be captured by our approach when utilizing suitable refute operators. In addition, we showcase how our general scheme can be employed to propose novel semantics in a systematic manner. This results in what we call <em>cooperative semantics</em> which stem from a novel refute operator we will introduce. We perform an in-depth investigation of basic properties of refute operators and to which extent the induced semantics inherit desirable properties from it. Among others, we show under which conditions i) a counterpart to Dung’s fundamental lemma can be inferred, ii) the generalized version of the grounded extension is unique, or iii) the generalized version of stable semantics does not collapse. Moreover, we contribute to a principle-based study of AF semantics by discussing properties tailored to assess the behavior of different refute operators. This includes an investigation of means to compare refute operators in terms of their aggressiveness. Finally, we conclude the study by reporting computational complexity results for basic reasoning tasks which hold in our general framework.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"352 ","pages":"Article 104456"},"PeriodicalIF":4.6,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OBDDs, SDDs, and circuits of bounded width: Completeness matters obdd、sdd和有界宽度电路：完整性问题

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-11-30 DOI: 10.1016/j.artint.2025.104458

Alexis De Colnet , Sebastian Ordyniak , Stefan Szeider

Ordered Binary Decision Diagrams (OBDDs) are dynamic data structures with many application areas. The literature suggested that OBDDs of bounded width equate to Boolean circuits of bounded pathwidth. In this paper, we show that this relationship holds only for complete OBDDs. Additionally, we demonstrate that similar limitations affect the claimed equivalence between Sentential Decision Diagrams (SDDs) of bounded width and Boolean circuits of bounded treewidth.

有序二元决策图（obdd）是一种动态数据结构，具有广泛的应用领域。文献认为有界宽度的obdd等于有界径宽的布尔电路。在本文中，我们证明了这种关系只适用于完整的obdd。此外，我们证明了类似的限制影响了有界宽度的句子决策图（sdd）和有界树宽度的布尔电路之间的等价性。

引用次数: 0

Federated neural nonparametric point processes 联邦神经非参数点过程

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-11-22 DOI: 10.1016/j.artint.2025.104454

Hui Chen , Xuhui Fan , Hengyu Liu , Yaqiong Li , Zhilin Zhao , Feng Zhou , Christopher John Quinn , Longbing Cao

Temporal point processes (TPPs) are effective for modeling event occurrences over time but struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose FedPP, a federated neural nonparametric point process model. FedPP integrates neural embeddings into sigmoidal Gaussian Cox processes (SGCPs) on the client side. SGCPs is a flexible and expressive class of TPPs, allowing FedPP to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism to communicate the distributions of kernel hyperparameters in SGCPs between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity. Extensive experiments demonstrate its superior performance in federated settings, showing global aggregation with the KL divergence and the Wasserstein distance.

时间点处理（TPPs）对于随着时间的推移而发生的事件建模是有效的，但是在联邦系统中难以处理稀疏和不确定的事件，其中隐私是一个主要问题。为了解决这个问题，我们提出了联邦神经非参数点过程模型FedPP。FedPP在客户端将神经嵌入集成到s型高斯Cox过程（SGCPs）中。sgcp是一种灵活且富有表现力的tpp类，允许FedPP生成高度灵活的强度函数，以捕获客户特定的事件动态和不确定性，同时有效地总结历史记录。对于全局聚合，FedPP引入了一种基于散度的机制，在服务器和客户端之间传递sgcp中内核超参数的分布，同时保持特定于客户端的参数在本地，以确保隐私和个性化。FedPP有效地捕获了事件的不确定性和稀疏性。大量的实验证明了它在联邦设置下的优越性能，显示出具有KL散度和Wasserstein距离的全局聚合。

{"title":"Federated neural nonparametric point processes","authors":"Hui Chen , Xuhui Fan , Hengyu Liu , Yaqiong Li , Zhilin Zhao , Feng Zhou , Christopher John Quinn , Longbing Cao","doi":"10.1016/j.artint.2025.104454","DOIUrl":"10.1016/j.artint.2025.104454","url":null,"abstract":"<div><div>Temporal point processes (TPPs) are effective for modeling event occurrences over time but struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose <em>FedPP</em>, a federated neural nonparametric point process model. FedPP integrates neural embeddings into sigmoidal Gaussian Cox processes (SGCPs) on the client side. SGCPs is a flexible and expressive class of TPPs, allowing FedPP to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism to communicate the distributions of kernel hyperparameters in SGCPs between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity. Extensive experiments demonstrate its superior performance in federated settings, showing global aggregation with the KL divergence and the Wasserstein distance.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"351 ","pages":"Article 104454"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Artificial Intelligence

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀