Artificial Intelligence最新文献_第2页

Multi-excitation projective simulation with a many-body physics-inspired inductive bias 多体物理感应偏置的多激励投影仿真

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.artint.2026.104489

Philip A. LeMaitre, Marius Krumm, Hans J. Briegel

{"title":"Multi-excitation projective simulation with a many-body physics-inspired inductive bias","authors":"Philip A. LeMaitre, Marius Krumm, Hans J. Briegel","doi":"10.1016/j.artint.2026.104489","DOIUrl":"10.1016/j.artint.2026.104489","url":null,"abstract":"<div><div>With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature that makes it difficult to interpret and understand their decisions. This problem led to the development of the field known as <em>eXplainable Artificial Intelligence</em> (XAI). One method in this field known as <em>Projective Simulation</em> (PS) models a chain-of-thought as a random walk of a particle on a graph with vertices that have concepts attached to them. While this description has various benefits, including the possibility of quantization, it cannot be naturally used to model thoughts that combine several concepts simultaneously. To overcome this limitation, we introduce <em>Multi-Excitation Projective Simulation</em> (MEPS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph. A definition for a dynamic hypergraph is put forward to describe the agent’s training history along with applications to AI and hypergraph visualization. An inductive bias inspired by the remarkably successful few-body interaction models used in quantum many-body physics is formalized for our classical MEPS framework and employed to tackle the exponential complexity associated with naive implementations of hypergraphs. We prove that our inductive bias reduces the complexity from exponential to polynomial, with the exponent representing the cutoff on the number of particles that can interact. We numerically apply our method to two toy model environments and a more complex scenario that models the diagnosis of a broken computer. These environments demonstrate the resource savings provided by an appropriate choice of the inductive bias, as well as showcasing aspects of interpretability. A quantum model for MEPS is also briefly outlined and some future directions for it are discussed.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"352 ","pages":"Article 104489"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033301","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

Symbolic pattern planning 符号模式规划

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.artint.2026.104482

Matteo Cardellini , Enrico Giunchiglia , Marco Maratea

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引用次数: 0

Settling the score: Portioning with cardinal preferences 解决问题：根据基本偏好进行分配

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.artint.2026.104487

Edith Elkind , Matthias Greger , Patrick Lederer , Warut Suksompong , Nicholas Teh

引用次数: 0

Corrigendum to “Kernel-Bounded Clustering: Achieving the Objective of Spectral Clustering without Eigendecomposition” [Artificial Intelligence 350 (2026) 104440] “核有界聚类：实现无特征分解的谱聚类目标”的勘误表[人工智能350 (2026)104440]

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.artint.2025.104473

Hang Zhang , Kai Ming Ting , Ye Zhu

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

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.artint.2025.104472

Eduard Eiben , Robert Ganian , Thekla Hamm , Viktoriia Korchemna

{"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":"2026-02-01","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

Disentangling data distribution for optimal and communication-efficient federated learning 面向最优通信高效联邦学习的解纠缠数据分布

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-11-21 DOI: 10.1016/j.artint.2025.104455

Xinyuan Zhao , Hanlin Gu , Lixin Fan , Yuxing Han , Qiang Yang

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

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.artint.2025.104470

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

{"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":"2026-02-01","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

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

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-11-30 DOI: 10.1016/j.artint.2025.104458

Alexis De Colnet , Sebastian Ordyniak , Stefan Szeider

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

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.artint.2025.104459

Luca Marzari , Francesco Leofante , Ferdinando Cicalese , Alessandro Farinelli

{"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":"2026-02-01","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

Artificial Intelligence Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.artint.2025.104468

Nathan R. Sturtevant , Shahaf Shperberg , Ariel Felner

引用次数: 0