International Journal of Intelligent Systems最新文献

{"title":"Beyond the Repertory Grid: New Approaches to Constructivist Knowledge Acquisition Tool Development","authors":"Jeffrey M. Bradshaw, Kenneth M. Ford, Jack R. Adams‐Webber, John H. Boose","doi":"10.1002/j.1098-111x.1993.tb00007.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00007.x","url":null,"abstract":"<jats:italic>Personal construct theory</jats:italic> provides both a plausible theoretical foundation for knowledge acquisition and a practical approach to modeling. Yet, only a fraction of the ideas latent in this theory have been tapped. Recently, several researchers have been taking another look at the theory, to discover new ways that it can shed light on the foundations and practice of knowledge acquisition. These efforts have led to the development of a new generation of constructivist knowledge acquisition systems: DDUCKS, ICONKAT, and KSSn/KRS. These tools extend repertory grid techniques in various ways and integrate them with ideas springing from complementary perspectives. New understandings of relationships between personal construct theory, assimilation theory, logic, semantic networks, and decision analysis have formed the underpinnings of these systems. Theoretical progress has fostered practical development in system architecture, graphical forms of knowledge representation, analysis and induction techniques, and group use of knowledge acquisition tools.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"39 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547050","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":"Closing the Gap Between Modeling to Make Sense and Modeling to Implement Systems","authors":"Marc Linster","doi":"10.1002/j.1098-111x.1993.tb00004.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00004.x","url":null,"abstract":"We view knowledge acquisition for knowledge‐based systems as a constructive model‐building process. From this view we derive several requirements for knowledge modeling environments. We concentrate on those requirements that arise if one wants to support both <jats:italic>modeling to make sense</jats:italic> and <jats:italic>modeling to implement systems</jats:italic> with a single language. For example, among other things, such languages should support multifaceted, bottom‐up construing of observed behavior and they should have operational semantics. We introduce the operational modeling language OMOS, an experimental study that—in a KADS‐like fashion—allows multifaceted model building from a method and a domain point of view, but, unlike KADS conceptual models, results in directly operational systems. Finally, we compare OMOS to other recent developments to highlight differences in the approaches.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547054","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":"Learning Simple Causal Structures1","authors":"Dan Geiger, Azaria Paz, Judea Pearl","doi":"10.1002/j.1098-111x.1993.tb00005.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00005.x","url":null,"abstract":"Humans use knowledge of causation to derive dependencies among events of interest. The converse task, that of inferring causal relationships from patterns of dependencies, is far less understood. This article established conditions under which the directionality of some dependencies is uniquely dictated by probabilistic information—an essential prerequisite for attributing a causal interpretation to these dependencies. An efficient algorithm is developed that, given data generated by an undisclosed simple causal schema, recovers the structure of that schema, as well as the directionality of all links that are uniquely orientable. A simple schema is represented by a directed acyclic graph (dag) where every pair of nodes with a common direct child have no common ancestor nor is one an ancestor of the other. Trees, singly connected dags, and directed bi‐partite graphs are examples of simple dags. Conditions ensuring the correctness of this recovery algorithm are provided.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547052","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":"Operator Assistant Systems: An Experimental Approach Using a Telerobotics Application*","authors":"Guy A. Boy, Nathalie Mathé","doi":"10.1002/j.1098-111x.1993.tb00006.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00006.x","url":null,"abstract":"This article presents a knowledge‐based system methodology for developing Operator Assistant (OA) systems in dynamic and interactive environments. Recent results in human error studies show that automation appears to be the most critical factor in human‐machine interaction problems. This is a problem both of training and design, which is the subject of this article. Design includes both design of the system to be controlled and design of procedures for operating this system. A specific knowledge representation is proposed for representing the corresponding system and operational knowledge. This representation is based on the situation recognition and analytical reasoning paradigm (SRAR). It tries to make explicit common factors involved in both human and machine intelligence, including perception and reasoning. An OA system based on this representation has been developed for space telerobotics. Simulations have been carried out with astronauts and the resulting protocols have been analyzed. Results show the relevance of the approach and have been used for improving the knowledge representation and the OA architecture. © 1993 John Wiley &amp; Sons, Inc.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547053","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":"A Formalization of Knowledge‐Level Models for Knowledge Acquisition","authors":"Hans Akkermans, Frank van Harmelen, Guus Schreiber, Bob Wielinga","doi":"10.1002/j.1098-111x.1993.tb00003.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00003.x","url":null,"abstract":"This article defines second‐generation knowledge acquisition as a modeling activity that is knowledge‐level oriented. Knowledge‐level models of expert reasoning represent an important output of the knowledge‐acquisition process, since they describe, in a conceptual and implementation‐independent fashion, the different roles and types of knowledge required for a problem‐solving task. We argue that a formalization of such models enhances knowledge acquisition, and in particular the conceptualization phase, by rendering currently informal concepts and intuitions more precise, thus also contributing to a more solid basis for KBS design, validation, and maintenance. A framework is constructed for the formal specification of knowledge‐level models. The proposed formalism, called <jats:sc>ml<jats:sup>2</jats:sup></jats:sc>, has been inspired by the <jats:sc>kads</jats:sc> methodology for KBS development, and aims at expressing different roles and types of knowledge components through employing an order‐sorted logic, a modular structuring of theories, and a meta‐level organization of knowledge, comprising “enlarged” reflection rules and a “meaningful” naming relation. An application of the formal specification method to heuristic classification is given. Issues relating to the epistemological adequacy and the computational tractability of formalized knowledge‐level models are discussed.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546958","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":"Modeling as Framework for Knowledge Acquisition Methodologies and Tools","authors":"Brian R. Gaines, Mildred L. G. Shaw, J. Brian Woodward","doi":"10.1002/j.1098-111x.1993.tb00002.x","DOIUrl":"https://doi.org/10.1002/j.1098-111x.1993.tb00002.x","url":null,"abstract":"This article develops a classification of the sources and types of models developed in knowledge engineering, and uses it to provide a framework within which knowledge acquisition methodologies and tools can be discussed and analyzed. Much of the early work on knowledge acquisition assumed that human expertise is based on “mental models” of domains and problem‐solving techniques, and that these can be elicited and transferred to an expert system. The approach taken here is to focus instead on the knowledge engineer's modeling process, his or her conceptual models of systems associated with the expert's skill, and their sources and types. This leads to a comprehensive account of knowledge‐based system development encompassing classical systems analysis, cognitive processes, linguistic representations, and the formalization of knowledge for computer application.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"8 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547051","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":"A Container-Based Cloud Broker for Effective Service Provisioning in Multicloud Environment","authors":"Vinothiyalakshmi P.,&nbsp;Rajganesh Nagarajan,&nbsp;Ramkumar Thirunavukarasu,&nbsp;Arun Pandian J.,&nbsp;Evans Kotei","doi":"10.1155/int/1009713","DOIUrl":"https://doi.org/10.1155/int/1009713","url":null,"abstract":"<div>\u0000 <p>Container-based cloud brokers are third-party services that act as an intermediate entity between users and multiple cloud providers. The cloud brokers intended to perform discovery and provisioning of cloud services with an affordable pricing scheme. As cloud services can be provisioned on-demand basis for multiple users, the cloud brokers are unable to provide the most suited services to the users on time. To address this issue, the proposed work introduces a novel approach for efficient cloud service provisioning by utilizing container-based cloud service brokerage and implementing service arbitrage across various cloud providers. A microservice architecture-based service discovery mechanism is developed which incorporates a service registry for tracking newly available services from the providers. Docker containers are employed to orchestrate the services, which ensures streamlined management and deployment of offered services. Further, the proposed system recommends and evaluates the services to the cloud users based on probability matrices, mapping matrices, and user feedback. The performance of the proposed model is compared with existing techniques, namely, rough multidimensional matrix (RMDM) and similarity-enhanced hybrid group recommendation approach (HGRA). Experimental results show that the proposed model outperforms the existing models in terms of clustering accuracy and execution time.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1009713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519761","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":"Interval-Valued Probabilistic Dual Hesitant Fuzzy Muirhead Mean Aggregation Operators and Their Applications in Regenerative Energy Source Selection","authors":"Muhammad Qiyas,&nbsp;Muhammad Naeem,&nbsp;Zahid Khan,&nbsp;Samuel Okyer","doi":"10.1155/int/8892299","DOIUrl":"https://doi.org/10.1155/int/8892299","url":null,"abstract":"<div>\u0000 <p>As an effective addition to the hesitant fuzzy set (HFS), a probabilistic dual hesitant fuzzy set (PDHFS) has been designed in this paper. PDHFS would be an improved version of the dual hesitant fuzzy set (DHFS) where both membership and nonmembership hesitant quality is considered for all its probability of existence. Additional information on the degree of acceptance or rejection contains such allocated probabilities. More conveniently, we create a comprehensive type of PDHFS called interval-valued PDHFS (IVPDHFS) to interpret the probability data that exist in the hesitancy. This study describes several basic operating laws by stressing the advantages and enriching the utility of IVPDHFS in MAGDM. To aggregate IVPDHF information in MAGDM problems and extend its applications, we present the Muirhead mean (MM) operator of IVPDHFSs and study some attractive properties of the suggested operator. Besides that, in order to compute attribute weights, a new organizational framework is designed by using partial knowledge of the decision makers (DMs). Subsequently, a standardized technique with the suggested operator for MAGDM is introduced, and the realistic usage of the operator is illustrated by the use of a problem of regenerative energy source selection. We discuss the influence of the parameter vector on the ranking results. Finally, to address the benefits and limitations of the recommended MAGDM approach, the findings of the proposal are contrasted with other approaches.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8892299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519630","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":"A Hybrid TLBO–XGBoost Model With Novel Labeling for Bitcoin Price Prediction","authors":"Elnaz Radmand,&nbsp;Jamshid Pirgazi,&nbsp;Ali Ghanbari Sorkhi","doi":"10.1155/int/6674437","DOIUrl":"https://doi.org/10.1155/int/6674437","url":null,"abstract":"<div>\u0000 <p>In the digital currency market, including Bitcoin, price prediction using artificial intelligence (AI) and machine learning (ML) is critical but challenging. Conventional methods such as technical analysis (based on historical market data) and fundamental analysis (based on economic variables) suffer from data noise, processing delays, and insufficient data. To make predictions more accurate, faster, and able to handle more data, the suggested method combines several steps: extracting important information, labeling it, choosing the best features, merging different models, and fine-tuning the model settings. Based on the price data, this approach initially generates 5 labels with a new labeling method based on the percentage of average price changes in several days and generates signals (hold, buy, sell, strong sell, and strong buy). Thereafter, it extracts 768 features from technical studies using the TA-Lib library and from an authoritative site. The TLBOA algorithm, which does not get stuck in the local optimum with two updates, was used to select and reduce features to 15 to avoid overfitting. A variety of ML models, including support vector machine and Naive Bayes, use these selected features for training. By using the evolutionary DE algorithm to optimize the XGBoost meta-parameters, we increased the accuracy by 1%–4%. The proposed strategy has performed better than other models, such as XGBoost with 85.66% and gradient boosting with 84.15%, and has achieved an accuracy of 91%–92%.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6674437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519631","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":"Framework for Quantum-Based Deepfake Video Detection (Without Audio)","authors":"Atul Pandey,&nbsp;Bhawana Rudra,&nbsp;Rajesh Kumar Krishnan","doi":"10.1155/int/3990069","DOIUrl":"https://doi.org/10.1155/int/3990069","url":null,"abstract":"<div>\u0000 <p>Artificial intelligence (AI) has made human tasks easier compared to earlier days. It has revolutionized various domains, from paper drafting to video editing. However, some individuals exploit AI to create deceptive content, such as fake videos, audios, and images, to mislead others. To address this, researchers and large corporations have proposed solutions for detecting fake content using classical deep learning models. However, these models often suffer from a large number of trainable parameters, which leads to large model sizes and, consequently, computational intensive. To overcome these limitations, we propose various hybrid classical–quantum models that use a classical pre-trained model as a front-end feature extractor, followed by a quantum-based LSTM network, that is, QLSTM. These pre-trained models are based on the ResNet architecture, such as ResNet34, 50, and 101. We have compared the performance of the proposed models with their classical counterparts. These proposed models combine the strengths of classical and quantum systems for the detection of deepfake video (without audio). Our results indicate that the proposed models significantly reduce the number of trainable parameters, as well as quantum long short-term memory (QLSTM) parameters, which leads to a smaller model size than the classical models. Despite the reduced parameter, the performance of the proposed models is either superior to or comparable with that of their classical equivalent. The proposed hybrid quantum models, that is, ResNet34-QLSTM, ResNet50-QLSTM, and ResNet101-QLSTM, achieve a reduction of approximately 1.50%, 4.59%, and 5.24% in total trainable parameters compared to their equivalent classical models, respectively. Additionally, QLSTM linked with the proposed models reduces its trainable parameters by 99.02%, 99.16%, and 99.55%, respectively, compared to equivalent classical LSTM. This significant reduction highlights the efficiency of the quantum-based network in terms of resource usage. The trained model sizes of the proposed models are 81.35, 88.06, and 162.79, and their equivalent classical models are 82.59, 92.28, and 171.76 in MB, respectively.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3990069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492724","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}