International Journal of Approximate Reasoning最新文献

{"title":"A dissection of the monotonicity property of binary operations from a dominance point of view","authors":"Yuntian Wang ,&nbsp;Lemnaouar Zedam ,&nbsp;Bao Qing Hu ,&nbsp;Bernard De Baets","doi":"10.1016/j.ijar.2024.109304","DOIUrl":"10.1016/j.ijar.2024.109304","url":null,"abstract":"<div><div>In this paper, we expound weaker forms of increasingness of binary operations on a lattice by reducing the number of variables involved in the classical formulation of the increasingness property as seen from the viewpoint of dominance between binary operations. We investigate the relationships among these weaker forms. Furthermore, we demonstrate the role of these weaker forms in characterizing the meet and join operations of a lattice and a chain in particular. Finally, we provide ample generic examples.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109304"},"PeriodicalIF":3.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Selected papers from the First International Joint Conference on Conceptual Knowledge Structures","authors":"Inma P. Cabrera,&nbsp;Sébastien Ferré,&nbsp;Sergei Obiedkov","doi":"10.1016/j.ijar.2024.109303","DOIUrl":"10.1016/j.ijar.2024.109303","url":null,"abstract":"","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109303"},"PeriodicalIF":3.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Iterative algorithms for solving one-sided partially observable stochastic shortest path games","authors":"Petr Tomášek,&nbsp;Karel Horák,&nbsp;Branislav Bošanský","doi":"10.1016/j.ijar.2024.109297","DOIUrl":"10.1016/j.ijar.2024.109297","url":null,"abstract":"<div><div>Real-world scenarios often involve dynamic interactions among competing agents, where decisions are made considering actions taken by others. These situations can be modeled as partially observable stochastic games (<span>POSG</span>s), with zero-sum variants capturing strictly competitive interactions (e.g., security scenarios). While such models address a broad range of problems, they commonly focus on infinite-horizon scenarios with discounted-sum objectives. Using the discounted-sum objective, however, can lead to suboptimal solutions in cases where the length of the interaction does not directly affect the gained rewards of the players.</div><div>We thus focus on games with undiscounted objective and an indefinite horizon where every realization of the game is guaranteed to terminate after some unspecified number of turns. To manage the computational complexity of solving <span>POSG</span>s in general, we restrict to games with one-sided partial observability where only one player has imperfect information while their opponent is provided with full information about the current situation. We introduce two novel algorithms based on the heuristic search value iteration (<span>HSVI</span>) algorithm that iteratively solve sequences of easier-to-solve approximations of the game using fundamentally different approaches for constructing the sequences: (1) in <span>GoalHorizon</span>, the game approximations are based on a limited number of turns in which players can change their actions, (2) in <span>GoalDiscount</span>, the game approximations are constructed using an increasing discount factor. We provide theoretical qualitative guarantees for algorithms, and we also experimentally demonstrate that these algorithms are able to find near-optimal solutions on pursuit-evasion games and a game modeling privilege escalation problem from computer security.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109297"},"PeriodicalIF":3.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Uncertainty-based knowledge distillation for Bayesian deep neural network compression","authors":"Mina Hemmatian ,&nbsp;Ali Shahzadi ,&nbsp;Saeed Mozaffari","doi":"10.1016/j.ijar.2024.109301","DOIUrl":"10.1016/j.ijar.2024.109301","url":null,"abstract":"<div><div>Deep learning models have been widely employed across various fields. In real-world scenarios, especially safety-critical applications, quantifying uncertainty is as crucial as achieving high accuracy. To address this concern, Bayesian deep neural networks (BDNNs) emerged to estimate two different types of uncertainty: Aleatoric and Epistemic. Nevertheless, implementing a BDNN on resource-constrained devices poses challenges due to the substantial computational and storage costs imposed by approximation inference techniques. Thus, efficient compression methods should be utilized. We propose an uncertainty-based knowledge distillation method to compress BDNNs. Knowledge distillation is a model compression technique that involves transferring knowledge from a complex network, known as the teacher network, to a simpler one, referred to as the student network. Our method incorporates uncertainty into knowledge distillation to address situations where inappropriate teacher supervision undermines compression performance. We utilize the Epistemic uncertainty of teacher predictions to tailor supervision for each sample individually to take into account teacher's limited knowledge. Additionally, we adjust the temperature parameter of the distillation process for each sample based on the Aleatoric uncertainty of the teacher predictions, ensuring that the student receives appropriate supervision even in the presence of ambiguous data. As a result, the proposed method enables the Bayesian student network to be trained under both appropriate supervision of the Bayesian teacher network and ground truth labels. We evaluated our method on the CIFAR-10, CIFAR-100, and RAF-DB datasets, demonstrating notable improvements in accuracy over state-of-the-art knowledge distillation-based methods. Furthermore, the robustness of our approach was assessed through testing weakly trained teacher networks and the analysis of blurred and low-resolution data, which have high uncertainty. Experimental results show that the proposed method outperformed existing methods.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109301"},"PeriodicalIF":3.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Distributed fusion-based algorithms for learning high-dimensional Bayesian Networks: Testing ring and star topologies","authors":"Jorge D. Laborda ,&nbsp;Pablo Torrijos ,&nbsp;José M. Puerta ,&nbsp;José A. Gámez","doi":"10.1016/j.ijar.2024.109302","DOIUrl":"10.1016/j.ijar.2024.109302","url":null,"abstract":"<div><div>Learning Bayesian Networks (BNs) from high-dimensional data is a complex and time-consuming task. Although there are approaches based on horizontal (instances) or vertical (variables) partitioning in the literature, none can guarantee the same theoretical properties as the Greedy Equivalence Search (GES) algorithm, except those based on the GES algorithm itself. This paper proposes a parallel distributed framework that uses GES as its local learning algorithm, obtaining results similar to those of GES and guaranteeing its theoretical properties but requiring less execution time. The framework involves splitting the set of all possible edges into clusters and constraining each framework node to only work with the received subset of edges. The global learning process is an iterative algorithm that carries out rounds until a convergence criterion is met. We have designed a ring and a star topology to distribute node connections. Regardless of the topology, each node receives a BN as input; it then fuses it with its own BN model and uses the result as the starting point for a local learning process, limited to its own subset of edges. Once finished, the result is then sent to another node as input. Experiments were carried out on a large repertory of domains, including large BNs up to more than 1000 variables. Our results demonstrate our proposal's effectiveness compared to GES and its fast version (fGES), generating high-quality BNs in less execution time.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109302"},"PeriodicalIF":3.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Belief rule learning and reasoning for classification based on fuzzy belief decision tree","authors":"Lianmeng Jiao ,&nbsp;Han Zhang ,&nbsp;Xiaojiao Geng ,&nbsp;Quan Pan","doi":"10.1016/j.ijar.2024.109300","DOIUrl":"10.1016/j.ijar.2024.109300","url":null,"abstract":"<div><div>The belief rules which extend the classical fuzzy IF-THEN rules with belief consequent parts have been widely used for classifier design due to their capabilities of building linguistic models interpretable to users and addressing various types of uncertainty. However, in the rule learning process, a high number of features generally results in a belief rule base with large size, which degrades both the classification accuracy and the model interpretability. Motivated by this challenge, the decision tree building technique which implements feature selection and model construction jointly is introduced in this paper to learn a compact and accurate belief rule base. To this end, a new fuzzy belief decision tree (FBDT) with fuzzy feature partitions and belief leaf nodes is designed: a fuzzy information gain ratio is first defined as the feature selection criterion for node fuzzy splitting and then the belief distributions are introduced to the leaf nodes to characterize the class uncertainty. Based on the initial rules extracted from the constructed FBDT, a joint optimization objective considering both classification accuracy and model interpretability is then designed to further reduce the rule redundancy. Experimental results based on real datasets show that the proposed FBDT-based classification method has much smaller rule base and better interpretability than other rule-based methods on the premise of competitive accuracy.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109300"},"PeriodicalIF":3.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Chain graph structure learning based on minimal c-separation trees","authors":"Luyao Tan ,&nbsp;Yi Sun ,&nbsp;Yu Du","doi":"10.1016/j.ijar.2024.109298","DOIUrl":"10.1016/j.ijar.2024.109298","url":null,"abstract":"<div><div>Chain graphs are a comprehensive class of graphical models that describe conditional independence information, encompassing both Markov networks and Bayesian networks as particular instances. In this paper, we propose a computationally feasible algorithm for the structural learning of chain graphs based on the idea of “dividing and conquering”, decomposing the learning problem into a set of minimal scale problems on its decomposed subgraphs. To this aim, we propose the concept of minimal c-separation trees in chain graphs and provide a mechanism to generate them, based on which we conduct structural learning using the divide and conquer technique. Experimental studies under various settings demonstrate that the presented structural learning algorithm for chain graphs generally outperforms existing methods. The code of this work is available at <span><span>https://github.com/luyaoTan/mtlc</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109298"},"PeriodicalIF":3.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Three-way conceptual knowledge updating in incomplete contexts","authors":"Ruisi Ren ,&nbsp;Ling Wei ,&nbsp;Jianjun Qi ,&nbsp;Xiaosong Wei","doi":"10.1016/j.ijar.2024.109299","DOIUrl":"10.1016/j.ijar.2024.109299","url":null,"abstract":"<div><div>We usually encounter incomplete data in daily life due to the uncertainty of data and limitation of data acquisition technology. In formal concept analysis, the incomplete formal context is used to reflect uncertain relation between objects and attributes caused by missing data. The conceptual knowledge of the incomplete formal context is presented by a kind of three-way concept called partially-known formal concept. As time passes and technology matures, some initially missing data becomes obtainable, the incomplete formal context is updated accordingly, and the corresponding concepts change as well. However, obtaining partially-known concepts from the updated context based on definition is time-consuming and does not fully utilize the conceptual knowledge implicit in the original context. In order to make full use of existing conceptual knowledge and acquire new concepts quickly and efficiently, we discuss how to obtain new partially-known formal concepts by updating original partially-known formal concepts, and design corresponding concept updating algorithms. Finally, through data experiments, we validate that our proposed concept update algorithm can significantly improve the efficiency of concept acquisition, especially when the updating rate is small.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109299"},"PeriodicalIF":3.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Approximations of system W for inference from strongly and weakly consistent belief bases","authors":"Jonas Haldimann,&nbsp;Christoph Beierle","doi":"10.1016/j.ijar.2024.109295","DOIUrl":"10.1016/j.ijar.2024.109295","url":null,"abstract":"<div><p>In this article, we investigate approximations of the inductive inference operator system W that has been shown to exhibit desirable inference properties and to extend both system Z, and thus rational closure, and c-inference. For versions of these inference operators that are extended to also cover inference from belief bases that are only weakly consistent, we first show that extended system Z and extended c-inference are captured by extended system W. Then we introduce general functions for generating inductive inference operators: the combination of two inductive inference operators by union, and the completion of an inductive inference operator by an arbitrary set of axioms. We construct the least inductive inference operator extending system Z and c-inference that is closed under system P and show that it is still strictly extended by extended system W. Furthermore, we introduce an inductive inference operator that strictly extends extended system W and that is strictly extended by lexicographic inference. This leads to a comprehensive map of inference relations between rational closure and extended c-inference on the one side and lexicographic inference on the other side with extended system W and its approximations at its centre, where all relationships also hold for the unextended versions.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109295"},"PeriodicalIF":3.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X24001828/pdfft?md5=aea167416445f5cb415a0340cf6d3109&pid=1-s2.0-S0888613X24001828-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Fuzzy implications-based transformation approaches from semi-three-way decision spaces to three-way decision spaces and their applications","authors":"Yiding Wang ,&nbsp;Junsheng Qiao ,&nbsp;Tengbiao Li","doi":"10.1016/j.ijar.2024.109296","DOIUrl":"10.1016/j.ijar.2024.109296","url":null,"abstract":"<div><p>Three-way decision spaces, as an important component of three-way decisions, greatly enrich their theoretical development and application prospects. Meanwhile, fuzzy implications, as a vital class of fuzzy logic connectives, have made great contributions to the solution of practical problems, especially complex decision-making problems. This paper considers the collaborative effect of the two, which inject new vitality into the theoretical development and application prospects of fuzzy implications and three-way decision spaces. As a vital component of three-way decision spaces, (semi-)decision evaluation functions have been widely studied based on fuzzy logic connectives and become a research hotspot. Specifically, this paper focuses on fuzzy implications-based transformation approaches from semi-three-way decision spaces to three-way decision spaces and their applications. Firstly, we present some novel fuzzy implications-based transformation approaches from semi-decision evaluation functions to decision evaluation functions, and construction approaches of semi-decision evaluation functions involving the existing semi-decision evaluation functions, fuzzy sets, interval-valued fuzzy sets and fuzzy relations. Secondly, we discuss the relationship between our approaches and the known construction approaches of three-way decision spaces. Notably, our approaches cover all existing approaches except the uninorms-based approaches. Finally, by the experiment results, we obtain our approaches are feasible, effective, superior to the known three-way decision spaces approaches and have good anti-noise ability. And, the parameter <em>ρ</em> of our approaches is also effective and stable.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109296"},"PeriodicalIF":3.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}