Artificial Intelligence最新文献_第6页

A simple proof-theoretic characterization of stable models: Reduction to difference logic and experiments 稳定模型的一个简单的证明理论表征：归约到差分逻辑和实验

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-12-24 DOI: 10.1016/j.artint.2024.104276

Martin Gebser , Enrico Giunchiglia , Marco Maratea , Marco Mochi

{"title":"A simple proof-theoretic characterization of stable models: Reduction to difference logic and experiments","authors":"Martin Gebser , Enrico Giunchiglia , Marco Maratea , Marco Mochi","doi":"10.1016/j.artint.2024.104276","DOIUrl":"10.1016/j.artint.2024.104276","url":null,"abstract":"<div><div>Stable models of logic programs have been studied and characterized in relation with other formalisms by many researchers. As already argued in previous papers, such characterizations are interesting for diverse reasons, including theoretical investigations and the possibility of leading to new algorithms for computing stable models of logic programs. At the theoretical level, complexity and expressiveness comparisons have brought about fundamental insights. Beyond that, practical implementations of the developed reductions enable the use of existing solvers for other logical formalisms to compute stable models. In this paper, we first provide a simple characterization of stable models that can be viewed as a proof-theoretic counterpart of the standard model-theoretic definition. We further show how it can be naturally encoded in difference logic. Such an encoding, compared to the existing reductions to classical logics, does not require Boolean variables. Then, we implement our novel translation to a Satisfiability Modulo Theories (SMT) formula. We finally compare our approach, employing the SMT solver <span>yices</span>, to the translation-based ASP solver <span>lp2diff</span> and to <span>clingo</span> on domains from the “Basic Decision” track of the 2017 Answer Set Programming competition. The results show that our approach is competitive to and often better than <span>lp2diff</span>, and that it can also be faster than <span>clingo</span> on non-tight domains.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104276"},"PeriodicalIF":5.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defying catastrophic forgetting via influence function 通过影响函数对抗灾难性遗忘

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-11-27 DOI: 10.1016/j.artint.2024.104261

Rui Gao, Weiwei Liu

引用次数: 0

Human-AI coevolution 人类与人工智能的共同进化

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-11-13 DOI: 10.1016/j.artint.2024.104244

Dino Pedreschi , Luca Pappalardo , Emanuele Ferragina , Ricardo Baeza-Yates , Albert-László Barabási , Frank Dignum , Virginia Dignum , Tina Eliassi-Rad , Fosca Giannotti , János Kertész , Alistair Knott , Yannis Ioannidis , Paul Lukowicz , Andrea Passarella , Alex Sandy Pentland , John Shawe-Taylor , Alessandro Vespignani

{"title":"Human-AI coevolution","authors":"Dino Pedreschi , Luca Pappalardo , Emanuele Ferragina , Ricardo Baeza-Yates , Albert-László Barabási , Frank Dignum , Virginia Dignum , Tina Eliassi-Rad , Fosca Giannotti , János Kertész , Alistair Knott , Yannis Ioannidis , Paul Lukowicz , Andrea Passarella , Alex Sandy Pentland , John Shawe-Taylor , Alessandro Vespignani","doi":"10.1016/j.artint.2024.104244","DOIUrl":"10.1016/j.artint.2024.104244","url":null,"abstract":"<div><div>Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: <em>(i)</em> outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; <em>(ii)</em> propose a reflection at the intersection between complexity science, AI and society; <em>(iii)</em> provide real-world examples for different human-AI ecosystems; and <em>(iv)</em> illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104244"},"PeriodicalIF":5.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chimeric U-Net – Modifying the standard U-Net towards explainability 嵌合 U-Net - 为实现可解释性而修改标准 U-Net

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-30 DOI: 10.1016/j.artint.2024.104240

Kenrick Schulze , Felix Peppert , Christof Schütte , Vikram Sunkara

{"title":"Chimeric U-Net – Modifying the standard U-Net towards explainability","authors":"Kenrick Schulze , Felix Peppert , Christof Schütte , Vikram Sunkara","doi":"10.1016/j.artint.2024.104240","DOIUrl":"10.1016/j.artint.2024.104240","url":null,"abstract":"<div><div>Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net, encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under:</div><div><span><span>https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorch</span><svg><path></path></svg></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104240"},"PeriodicalIF":5.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating multi-armed bandit with local search for MaxSAT 为 MaxSAT 整合多臂强盗和局部搜索

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-30 DOI: 10.1016/j.artint.2024.104242

Jiongzhi Zheng , Kun He , Jianrong Zhou , Yan Jin , Chu-Min Li , Felip Manyà

{"title":"Integrating multi-armed bandit with local search for MaxSAT","authors":"Jiongzhi Zheng , Kun He , Jianrong Zhou , Yan Jin , Chu-Min Li , Felip Manyà","doi":"10.1016/j.artint.2024.104242","DOIUrl":"10.1016/j.artint.2024.104242","url":null,"abstract":"<div><div>Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we introduce a new local search algorithm for these problems, named BandHS. It applies two multi-armed bandit (MAB) models to guide the search directions when escaping local optima. One MAB model is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, while the other MAB model is combined with all the literals in hard clauses to help the algorithm select suitable literals to satisfy the hard clauses. These two models enhance the algorithm's search ability in both feasible and infeasible solution spaces. BandHS also incorporates a novel initialization method that prioritizes both unit and binary clauses when generating the initial solutions. Moreover, we apply our MAB approach to the state-of-the-art local search algorithm NuWLS and to the local search component of the incomplete solver NuWLS-c-2023. The extensive experiments conducted demonstrate the excellent performance and generalization capability of the proposed method. Additionally, we provide analyses on the type of problems where our MAB method works well or not, aiming to offer insights and suggestions for its application. Encouragingly, our MAB method has been successfully applied in core local search components in the winner of the WPMS complete track of MaxSAT Evaluation 2023, as well as the runners-up of the incomplete track of MaxSAT Evaluations 2022 and 2023.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104242"},"PeriodicalIF":5.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577758","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

Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments 解释建模：通过推理句子中隐含的道德判断，使句子具有社会基础

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-28 DOI: 10.1016/j.artint.2024.104234

Liesbeth Allein, Maria Mihaela Truşcǎ, Marie-Francine Moens

{"title":"Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments","authors":"Liesbeth Allein, Maria Mihaela Truşcǎ, Marie-Francine Moens","doi":"10.1016/j.artint.2024.104234","DOIUrl":"10.1016/j.artint.2024.104234","url":null,"abstract":"<div><div>The social and implicit nature of human communication ramifies readers' understandings of written sentences. Single gold-standard interpretations rarely exist, challenging conventional assumptions in natural language processing. This work introduces the interpretation modeling (IM) task which involves modeling several interpretations of a sentence's underlying semantics to unearth layers of implicit meaning. To obtain these, IM is guided by multiple annotations of social relation and common ground - in this work approximated by reader attitudes towards the author and their understanding of moral judgments subtly embedded in the sentence. We propose a number of modeling strategies that rely on one-to-one and one-to-many generation methods that take inspiration from the philosophical study of interpretation. A first-of-its-kind IM dataset is curated to support experiments and analyses. The modeling results, coupled with scrutiny of the dataset, underline the challenges of IM as conflicting and complex interpretations are socially plausible. This interplay of diverse readings is affirmed by automated and human evaluations on the generated interpretations. Finally, toxicity analyses in the generated interpretations demonstrate the importance of IM for refining filters of content and assisting content moderators in safeguarding the safety in online discourse.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104234"},"PeriodicalIF":5.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The complexity of optimizing atomic congestion 优化原子拥塞的复杂性

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-22 DOI: 10.1016/j.artint.2024.104241

Cornelius Brand , Robert Ganian , Subrahmanyam Kalyanasundaram , Fionn Mc Inerney

{"title":"The complexity of optimizing atomic congestion","authors":"Cornelius Brand , Robert Ganian , Subrahmanyam Kalyanasundaram , Fionn Mc Inerney","doi":"10.1016/j.artint.2024.104241","DOIUrl":"10.1016/j.artint.2024.104241","url":null,"abstract":"<div><div>Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a <em>system-optimal</em> set of strategies—that is, a centrally planned routing that minimizes the average cost of agents—is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104241"},"PeriodicalIF":5.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven transcriptome profile-guided hit molecule generation 人工智能驱动的转录组图谱引导的热门分子生成

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-22 DOI: 10.1016/j.artint.2024.104239

Chen Li, Yoshihiro Yamanishi

{"title":"AI-driven transcriptome profile-guided hit molecule generation","authors":"Chen Li, Yoshihiro Yamanishi","doi":"10.1016/j.artint.2024.104239","DOIUrl":"10.1016/j.artint.2024.104239","url":null,"abstract":"<div><div><span><math><mi>D</mi><mi>e</mi><mspace></mspace><mi>n</mi><mi>o</mi><mi>v</mi><mi>o</mi></math></span> generation of bioactive and drug-like hit molecules is a pivotal goal in computer-aided drug discovery. While artificial intelligence (AI) has proven adept at generating molecules with desired chemical properties, previous studies often overlook the influence of disease-specific cellular environments. This study introduces GxVAEs, a novel AI-driven deep generative model designed to produce hit molecules from transcriptome profiles using dual variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from transcriptome profiles to guide the second VAE, MolVAE, in generating hit molecules. GxVAEs aim to bridge the gap between molecule generation and the biological context of disease, producing molecules that are biologically relevant within specific cellular environments or pathological conditions. Experimental results and case studies focused on hit molecule generation demonstrate that GxVAEs surpass current state-of-the-art methods, in terms of reproducibility of known ligands. This approach is expected to effectively find potential molecular structures with bioactivities across diverse disease contexts.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104239"},"PeriodicalIF":5.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gödel–Dummett linear temporal logic 哥德尔-杜密特线性时态逻辑

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-18 DOI: 10.1016/j.artint.2024.104236

Juan Pablo Aguilera , Martín Diéguez , David Fernández-Duque , Brett McLean

{"title":"Gödel–Dummett linear temporal logic","authors":"Juan Pablo Aguilera , Martín Diéguez , David Fernández-Duque , Brett McLean","doi":"10.1016/j.artint.2024.104236","DOIUrl":"10.1016/j.artint.2024.104236","url":null,"abstract":"<div><div>We investigate a version of linear temporal logic whose propositional fragment is Gödel–Dummett logic (which is well known both as a superintuitionistic logic and a t-norm fuzzy logic). We define the logic using two natural semantics: first a real-valued semantics, where statements have a degree of truth in the real unit interval, and second a ‘bi-relational’ semantics. We then show that these two semantics indeed define one and the same logic: the statements that are valid for the real-valued semantics are the same as those that are valid for the bi-relational semantics. This Gödel temporal logic does not have any form of the finite model property for these two semantics: there are non-valid statements that can only be falsified on an infinite model. However, by using the technical notion of a quasimodel, we show that every falsifiable statement is falsifiable on a finite quasimodel, yielding an algorithm for deciding if a statement is valid or not. Later, we strengthen this decidability result by giving an algorithm that uses only a polynomial amount of memory, proving that Gödel temporal logic is PSPACE-complete. We also provide a deductive calculus for Gödel temporal logic, and show this calculus to be sound and complete for the above-mentioned semantics, so that all (and only) the valid statements can be proved with this calculus.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"338 ","pages":"Article 104236"},"PeriodicalIF":5.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577891","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

Automatically designing counterfactual regret minimization algorithms for solving imperfect-information games 自动设计用于解决不完全信息博弈的反事实遗憾最小化算法

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2024-10-11 DOI: 10.1016/j.artint.2024.104232

Kai Li , Hang Xu , Haobo Fu , Qiang Fu , Junliang Xing

{"title":"Automatically designing counterfactual regret minimization algorithms for solving imperfect-information games","authors":"Kai Li , Hang Xu , Haobo Fu , Qiang Fu , Junliang Xing","doi":"10.1016/j.artint.2024.104232","DOIUrl":"10.1016/j.artint.2024.104232","url":null,"abstract":"<div><div>Strategic decision-making in imperfect-information games is an important problem in artificial intelligence. Counterfactual regret minimization (CFR), a family of iterative algorithms, has been the workhorse for solving these types of games since its inception. In recent years, a series of novel CFR variants have been proposed, significantly improving the convergence rate of vanilla CFR. However, most of these new variants are hand-designed by researchers through trial and error, often based on different motivations, which generally requires a tremendous amount of effort and insight. This work proposes AutoCFR, a systematic framework that meta-learns novel CFR algorithms through evolution, easing the burden of manual algorithm design. We first design a search language that is rich enough to represent various CFR variants. We then exploit a scalable regularized evolution algorithm with a set of acceleration techniques to efficiently search over the combinatorial space of algorithms defined by this language. The learned novel CFR algorithm can generalize to new imperfect-information games not seen during training and performs on par with or better than existing state-of-the-art CFR variants. In addition to superior empirical performance, we also theoretically show that the learned algorithm converges to an approximate Nash equilibrium. Extensive experiments across diverse imperfect-information games highlight the scalability, extensibility, and generalizability of AutoCFR, establishing it as a general-purpose framework for solving imperfect-information games.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104232"},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527869","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