Artificial Intelligence最新文献

{"title":"Polynomial calculus for optimization","authors":"Ilario Bonacina ,&nbsp;Maria Luisa Bonet ,&nbsp;Jordi Levy","doi":"10.1016/j.artint.2024.104208","DOIUrl":"10.1016/j.artint.2024.104208","url":null,"abstract":"<div><p>MaxSAT is the problem of finding an assignment satisfying the maximum number of clauses in a CNF formula. We consider a natural generalization of this problem to generic sets of polynomials and propose a weighted version of Polynomial Calculus to address this problem.</p><p>Weighted Polynomial Calculus is a natural generalization of the systems MaxSAT-Resolution and weighted Resolution. Unlike such systems, weighted Polynomial Calculus manipulates polynomials with coefficients in a finite field and either weights in <span><math><mi>N</mi></math></span> or <span><math><mi>Z</mi></math></span>. We show the soundness and completeness of weighted Polynomial Calculus via an algorithmic procedure.</p><p>Weighted Polynomial Calculus, with weights in <span><math><mi>N</mi></math></span> and coefficients in <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, is able to prove efficiently that Tseitin formulas on a connected graph are minimally unsatisfiable. Using weights in <span><math><mi>Z</mi></math></span>, it also proves efficiently that the Pigeonhole Principle is minimally unsatisfiable.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"337 ","pages":"Article 104208"},"PeriodicalIF":5.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001449/pdfft?md5=dff7733d570b1ecd6ce03a4fc7392fcb&pid=1-s2.0-S0004370224001449-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157921","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}

{"title":"Approximating problems in abstract argumentation with graph convolutional networks","authors":"Lars Malmqvist,&nbsp;Tangming Yuan,&nbsp;Peter Nightingale","doi":"10.1016/j.artint.2024.104209","DOIUrl":"10.1016/j.artint.2024.104209","url":null,"abstract":"<div><p>In this article, we present a novel approximation approach for abstract argumentation using a customized Graph Convolutional Network (GCN) architecture and a tailored training method. Our approach demonstrates promising results in approximating abstract argumentation tasks across various semantics, setting a new state of the art for performance on certain tasks. We provide a detailed analysis of approximation and runtime performance and propose a new scheme for evaluation. By advancing the state of the art for approximating the acceptability status of abstract arguments, we make theoretical and empirical advances in understanding the limits and opportunities for approximation in this field. Our approach shows potential for creating both general purpose and task-specific approximators and offers insights into the performance differences across benchmarks and semantics.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104209"},"PeriodicalIF":5.1,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001450/pdfft?md5=01068bd413e8769bb4469a717c95128e&pid=1-s2.0-S0004370224001450-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095894","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}

{"title":"Characterising harmful data sources when constructing multi-fidelity surrogate models","authors":"Nicolau Andrés-Thió ,&nbsp;Mario Andrés Muñoz ,&nbsp;Kate Smith-Miles","doi":"10.1016/j.artint.2024.104207","DOIUrl":"10.1016/j.artint.2024.104207","url":null,"abstract":"<div><p>Surrogate modelling techniques have seen growing attention in recent years when applied to both modelling and optimisation of industrial design problems. These techniques are highly relevant when assessing the performance of a particular design carries a high cost, as the overall cost can be mitigated via the construction of a model to be queried in lieu of the available high-cost source. The construction of these models can sometimes employ other sources of information which are both cheaper and less accurate. The existence of these sources however poses the question of which sources should be used when constructing a model. Recent studies have attempted to characterise harmful data sources to guide practitioners in choosing when to ignore a certain source. These studies have done so in a synthetic setting, characterising sources using a large amount of data that is not available in practice. Some of these studies have also been shown to potentially suffer from bias in the benchmarks used in the analysis. In this study, we approach the characterisation of harmful low-fidelity sources as an algorithm selection problem. We employ recently developed benchmark filtering techniques to conduct a bias-free assessment, providing objectively varied benchmark suites of different sizes for future research. Analysing one of these benchmark suites with the technique known as Instance Space Analysis, we provide an intuitive visualisation of when a low-fidelity source should be used. By performing this analysis using only the limited data available to train a surrogate model, we are able to provide guidelines that can be directly used in an applied industrial setting.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104207"},"PeriodicalIF":5.1,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001437/pdfft?md5=63ca7126b7bf14477005c50a202f2c7d&pid=1-s2.0-S0004370224001437-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083422","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}

{"title":"Is it possible to find the single nearest neighbor of a query in high dimensions?","authors":"Kai Ming Ting ,&nbsp;Takashi Washio ,&nbsp;Ye Zhu ,&nbsp;Yang Xu ,&nbsp;Kaifeng Zhang","doi":"10.1016/j.artint.2024.104206","DOIUrl":"10.1016/j.artint.2024.104206","url":null,"abstract":"<div><p>We investigate an open question in the study of the curse of dimensionality: Is it possible to find the single nearest neighbor of a query in high dimensions? Using the notion of (in)distinguishability to examine whether the feature map of a kernel is able to distinguish two distinct points in high dimensions, we analyze this ability of a metric-based Lipschitz continuous kernel as well as that of the recently introduced Isolation Kernel. Between the two kernels, we show that only Isolation Kernel has distinguishability and it performs consistently well in four tasks: indexed search for exact nearest neighbor search, anomaly detection using kernel density estimation, t-SNE visualization and SVM classification in both low and high dimensions, compared with distance, Gaussian and three other existing kernels.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104206"},"PeriodicalIF":5.1,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001425/pdfft?md5=a9c748954d0721f2e62c5fa4e574bf6e&pid=1-s2.0-S0004370224001425-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083423","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}

{"title":"Abstract argumentation frameworks with strong and weak constraints","authors":"Gianvincenzo Alfano,&nbsp;Sergio Greco,&nbsp;Domenico Mandaglio,&nbsp;Francesco Parisi,&nbsp;Irina Trubitsyna","doi":"10.1016/j.artint.2024.104205","DOIUrl":"10.1016/j.artint.2024.104205","url":null,"abstract":"<div><p>Dealing with controversial information is an important issue in several application contexts. Formal argumentation enables reasoning on arguments for and against a claim to decide on an outcome. Dung's abstract Argumentation Framework (AF) has emerged as a central formalism in argument-based reasoning. Key aspects of the success and popularity of Dung's framework include its simplicity and expressiveness. Integrity constraints help to express domain knowledge in a compact and natural way, thus keeping easy the modeling task even for problems that otherwise would be hard to encode within an AF. In this paper, we first explore two intuitive semantics based on Kleene and Lukasiewicz logics, respectively, for AF augmented with (strong) constraints—the resulting argumentation framework is called Constrained AF (CAF). Then, we propose a new argumentation framework called Weak constrained AF (WAF) that enhances CAF with weak constraints. Intuitively, these constraints can be used to find “optimal” solutions to problems defined through CAF. We provide a detailed complexity analysis of CAF and WAF, showing that strong constraints do not increase the expressive power of AF in most cases, while weak constraints systematically increase the expressive power of CAF (and AF) under several well-known argumentation semantics.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104205"},"PeriodicalIF":5.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001413/pdfft?md5=4e6a89453bad3925cb46537261fd58a9&pid=1-s2.0-S0004370224001413-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039953","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}

{"title":"Bisimulation between base argumentation and premise-conclusion argumentation","authors":"Jinsheng Chen ,&nbsp;Beishui Liao ,&nbsp;Leendert van der Torre","doi":"10.1016/j.artint.2024.104203","DOIUrl":"10.1016/j.artint.2024.104203","url":null,"abstract":"<div><p>The structured argumentation system that represents arguments by premise-conclusion pairs is called <em>premise-conclusion argumentation</em> (PA) and the one that represents arguments by their premises is called <em>base argumentation</em> (BA). To assess whether BA and PA have the same ability in argument evaluation by extensional semantics, this paper defines the notion of <em>extensional equivalence</em> between BA and PA. It also defines the notion of <em>bisimulation</em> between BA and PA and shows that bisimulation implies extensional equivalence. To illustrate how base argumentation, bisimulation and extensional equivalence can contribute to the study of PA, we prove some new results about PA by investigating the extensional properties of a base argumentation framework and exporting them to two premise-conclusion argumentation frameworks via bisimulation and extensional equivalence. We show that there are essentially three kinds of extensions in these frameworks and that the extensions in the two premise-conclusion argumentation frameworks are identical.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104203"},"PeriodicalIF":5.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020507","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}

{"title":"On generalized notions of consistency and reinstatement and their preservation in formal argumentation","authors":"Pietro Baroni ,&nbsp;Federico Cerutti ,&nbsp;Massimiliano Giacomin","doi":"10.1016/j.artint.2024.104202","DOIUrl":"10.1016/j.artint.2024.104202","url":null,"abstract":"<div><p>We present a conceptualization providing an original domain-independent perspective on two crucial properties in reasoning: consistency and reinstatement. They emerge as a pair of dual characteristics, representing complementary requirements on the outcomes of reasoning processes. Central to our formalization are two underlying parametric relations: incompatibility and reinstatement violation. Different instances of these relations give rise to a spectrum of consistency and reinstatement scenarios. As a demonstration of versatility and expressive power of our approach we provide a characterization of various abstract argumentation semantics which are expressed as combinations of distinct consistency and reinstatement constraints. Moreover, we conduct an investigation into preserving these essential properties across different reasoning stages. Specifically, we delve into scenarios where a labelling is derived from other labellings through a synthesis function, using the synthesis of argument justification as an illustrative instance. We achieve a general characterization of consistency preservation synthesis functions, while we unveil an impossibility result concerning reinstatement preservation, leading us to explore an alternative notion to ensure feasibility. Our exploration reveals a weakness in the traditional definition of argument justification, for which we propose a refined version overcoming this limitation.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104202"},"PeriodicalIF":5.1,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001383/pdfft?md5=2786e6cc7312f2e6c76d2f95b9cdcee1&pid=1-s2.0-S0004370224001383-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012117","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}

{"title":"Addressing maximization bias in reinforcement learning with two-sample testing","authors":"Martin Waltz ,&nbsp;Ostap Okhrin","doi":"10.1016/j.artint.2024.104204","DOIUrl":"10.1016/j.artint.2024.104204","url":null,"abstract":"<div><p>Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algorithms and can sometimes lead to dramatic performance decreases or even complete algorithmic failure. We frame the bias problem statistically and consider it an instance of estimating the maximum expected value (MEV) of a set of random variables. We propose the <em>T</em>-Estimator (TE) based on two-sample testing for the mean, that flexibly interpolates between over- and underestimation by adjusting the significance level of the underlying hypothesis tests. We also introduce a generalization, termed <em>K</em>-Estimator (KE), that obeys the same bias and variance bounds as the TE and relies on a nearly arbitrary kernel function. We introduce modifications of <em>Q</em>-Learning and the Bootstrapped Deep <em>Q</em>-Network (BDQN) using the TE and the KE, and prove convergence in the tabular setting. Furthermore, we propose an adaptive variant of the TE-based BDQN that dynamically adjusts the significance level to minimize the absolute estimation bias. All proposed estimators and algorithms are thoroughly tested and validated on diverse tasks and environments, illustrating the bias control and performance potential of the TE and KE.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104204"},"PeriodicalIF":5.1,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001401/pdfft?md5=5b6841aff0d8d49b8cc40332377d2f38&pid=1-s2.0-S0004370224001401-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020506","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}

{"title":"Modular control architecture for safe marine navigation: Reinforcement learning with predictive safety filters","authors":"Aksel Vaaler ,&nbsp;Svein Jostein Husa ,&nbsp;Daniel Menges ,&nbsp;Thomas Nakken Larsen ,&nbsp;Adil Rasheed","doi":"10.1016/j.artint.2024.104201","DOIUrl":"10.1016/j.artint.2024.104201","url":null,"abstract":"<div><p>Many autonomous systems are safety-critical, making it essential to have a closed-loop control system that satisfies constraints arising from underlying physical limitations and safety aspects in a robust manner. However, this is often challenging to achieve for real-world systems. For example, autonomous ships at sea have nonlinear and uncertain dynamics and are subject to numerous time-varying environmental disturbances such as waves, currents, and wind. There is increasing interest in using machine learning-based approaches to adapt these systems to more complex scenarios, but there are few standard frameworks that guarantee the safety and stability of such systems. Recently, predictive safety filters (PSF) have emerged as a promising method to ensure constraint satisfaction in learning-based control, bypassing the need for explicit constraint handling in the learning algorithms themselves. The safety filter approach leads to a modular separation of the problem, allowing the use of arbitrary control policies in a task-agnostic way. The filter takes in a potentially unsafe control action from the main controller and solves an optimization problem to compute a minimal perturbation of the proposed action that adheres to both physical and safety constraints. In this work, we combine reinforcement learning (RL) with predictive safety filtering in the context of marine navigation and control. The RL agent is trained on path-following and safety adherence across a wide range of randomly generated environments, while the predictive safety filter continuously monitors the agents' proposed control actions and modifies them if necessary. The combined PSF/RL scheme is implemented on a simulated model of Cybership II, a miniature replica of a typical supply ship. Safety performance and learning rate are evaluated and compared with those of a standard, non-PSF, RL agent. It is demonstrated that the predictive safety filter is able to keep the vessel safe, while not prohibiting the learning rate and performance of the RL agent.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104201"},"PeriodicalIF":5.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001371/pdfft?md5=32cb7040f174b219329c813dbac41fde&pid=1-s2.0-S0004370224001371-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985125","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}

{"title":"QCDCL with cube learning or pure literal elimination – What is best?","authors":"Benjamin Böhm ,&nbsp;Tomáš Peitl ,&nbsp;Olaf Beyersdorff","doi":"10.1016/j.artint.2024.104194","DOIUrl":"10.1016/j.artint.2024.104194","url":null,"abstract":"<div><p>Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving variants via proof complexity techniques. Our results show that almost all of the QCDCL variants are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104194"},"PeriodicalIF":5.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001309/pdfft?md5=5239acd648349c514fda83a672a66c32&pid=1-s2.0-S0004370224001309-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910632","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}