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Articles de revues sur le sujet « Decision refinement »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 19 juillet 2025

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Hillner, Bruce E. "Decision-theoretic Refinement Planning." Medical Decision Making 16, no. 4 (1996): 419–20. http://dx.doi.org/10.1177/0272989x9601600414.

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Sakurai, Shigeaki. "Refinement of fuzzy decision tree." IEEJ Transactions on Electronics, Information and Systems 117, no. 12 (1997): 1833–39. http://dx.doi.org/10.1541/ieejeiss1987.117.12_1833.

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Haddawy, Peter, Anhai Doan, and Charles E. Kahn. "Decision-theoretic Refinement Planning in Medical Decision Making." Medical Decision Making 16, no. 4 (1996): 315–25. http://dx.doi.org/10.1177/0272989x9601600402.

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Kline, Theresa J. B. "Refinement and Evaluation of the Decision-Making Questionnaire." Psychological Reports 78, no. 1 (1996): 151–62. http://dx.doi.org/10.2466/pr0.1996.78.1.151.

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The psychometric properties of the Decision-making Questionnaire which was designed to assess decision-making in an organizational context were investigated by administering the questionnaire to 54 undergraduate students. The dimensions measured are Effectiveness, Confidence, and Information used in making both tactical and strategic organizational decisions. The assessment of the Effectiveness scores consisted of examining item-to-total correlations, principal components analyses, and internal consistencies. Also, the relationships amongst all three dimensions measured by the scale as well as relationships of all three dimensions with measures of perceived opportunities and threats are reported.

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Damnjanović, Kaja, Sandra Ilić, Irena Pavlović, and Vera Novković. "Refinement of outcome bias measurement in the parental decision-making context." Europe’s Journal of Psychology 15, no. 1 (2019): 41–58. http://dx.doi.org/10.5964/ejop.v15i1.1698.

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The aim of this study was twofold: one was to test the impact of the involvement on the parental outcome bias, and the second was to refine the measurement of outcome bias, normally reported as the difference between evaluations of a single decision, with different outcomes assigned to it. We introduced the evaluation of a decision without an outcome, to induce theoretically normative evaluation, unbiased by outcome, from which the evaluation shift could be calculated in either direction. To test this refinement in the parental decision-making context, we produced childcare dilemmas with varying levels of complexity, since the rise of complexity induces stronger bias. Complexity was determined by the particular combination of two factors: parental involvement in a decision - the amount of motivation, interest and drive evoked by it – and whether the decision was health-related or not. We presented parents with the decisions for evaluation, followed by a positive and a negative outcome, and without an outcome. The results confirm the interaction between involvement and domain on decision evaluation. Highly involving decisions yielded weaker outcome bias than low-involvement decisions in both health and non-health domain. Results also confirm the validity of the proposed way of measuring OB, revealing that in some situations positive outcomes skew evaluations more than negative outcomes. Also, a highly-involving dilemma followed by negative outcome did not produce significantly different evaluation compared to evaluation of a decision without outcome. Thus, adding a neutral position rendered OB measurement more precise and our involvement-related insights more nuanced.

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Stone, Thomas, Seung-Kyum Choi, and Hemanth Amarchinta. "Structural model refinement under uncertainty using decision-maker preferences." Journal of Engineering Design 24, no. 9 (2013): 640–61. http://dx.doi.org/10.1080/09544828.2013.824560.

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Yerramareddy, Sudhakar, and Stephen C. Y. Lu. "Hierarchical and interactive decision refinement methodology for engineering design." Research in Engineering Design 4, no. 4 (1992): 227–39. http://dx.doi.org/10.1007/bf02032466.

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Shekaramiz, Mohammad, Todd K. Moon, and Jacob H. Gunther. "Exploration vs. Data Refinement via Multiple Mobile Sensors." Entropy 21, no. 6 (2019): 568. http://dx.doi.org/10.3390/e21060568.

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We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.

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Kaur, Iqbaldeep, and Rajesh Kumar Bawa. "Fuzzy based Schematic Component Selection Decision Search with OPAM-Ocaml Engine." Recent Patents on Computer Science 12, no. 3 (2019): 224–32. http://dx.doi.org/10.2174/2213275912666181210104742.

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Background: With an exponential increase in software online as well as offline, through each passing day, the task of digging out precise and relevant software components has become the need of the hour. There is no dearth of techniques used for the retrieval of software component from the available online and offline repositories in the conceptual as well as the empirical literature. However each of these techniques has its own set of limitations and suitability. Objective: The proposed technique gives concrete decision using schematic based search that gives better result and higher precision and recall values. Methods: In this paper, a component decision and retrieval engine called SR-SCRS (Schematic and Refinement based Software Component Retrieval System) has been presented using OPAM. OPAM is a github repository containing software components (packages), designed by OcamlPro. This search engine employs two retrieval techniques for a robust decision vis-o-vis Schematic-based search with fuzzy logic and Refinement-based search. The Schematic based search is based on matching the attribute values and the threshold of those values as given by the user. Thereafter the results are optimized to achieve the level of relevance using fuzzy logic. Refinement based search works on one particular attribute value. The experiments have been conducted and validated on OPAM dataset. Results: Precisely, the average precision of Schematic based search and Refinement based search is 60% and 27.86% which shows robust results. Conclusion: Hence, the performance and efficiency of the proposed work has been evaluated and compared with the other retrieval technique.

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Campbell, Felix S., Alon Silberstein, Julia Stoyanovich, and Yuval Moskovitch. "Query Refinement for Diverse Top-k Selection." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–27. http://dx.doi.org/10.1145/3654969.

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Database queries are often used to select and rank items as decision support for many applications. As automated decision-making tools become more prevalent, there is a growing recognition of the need to diversify their outcomes. In this paper, we define and study the problem of modifying the selection conditions of an ORDER BY query so that the result of the modified query closely fits some user-defined notion of diversity while simultaneously maintaining the intent of the original query. We show the hardness of this problem and propose a mixed-integer linear programming (MILP) based solution. We further present optimizations designed to enhance the scalability and applicability of the solution in real-life scenarios. We investigate the performance characteristics of our algorithm and show its efficiency and the usefulness of our optimizations.

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Chadha, Rohit, and Mahesh Viswanathan. "A counterexample-guided abstraction-refinement framework for markov decision processes." ACM Transactions on Computational Logic 12, no. 1 (2010): 1–49. http://dx.doi.org/10.1145/1838552.1838553.

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Kattenbelt, Mark, Marta Kwiatkowska, Gethin Norman, and David Parker. "A game-based abstraction-refinement framework for Markov decision processes." Formal Methods in System Design 36, no. 3 (2010): 246–80. http://dx.doi.org/10.1007/s10703-010-0097-6.

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Pathak, Ravi, and V. Vaidehi. "Complex Event Refinement by Statistical Augmentation Model." International Journal of Intelligent Information Technologies 11, no. 2 (2015): 55–69. http://dx.doi.org/10.4018/ijiit.2015040104.

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The uncertainty of decision making in event hierarchies of CEP can be due to unreliable data sources, lack of conformance that the event which is reported has actually occurred. Also the Complex Event models which are used to define complex events are inaccurate. When the uncertain event is used for deriving complex event, it propagates its uncertainty to a higher level of event hierarchy and causes uncertainty in reasoning. This paper proposes an event refinement model based on statistical approach to augment the events to minimize the error due to uncertainty for better decision making. The proposed augmented CEP (a-CEP) is found to perform better in terms of reduction in false alarm for continuous monitoring of patient in a remote health care application. The proposed model is implemented on Drools Fusion CEP Engine using Java and it is found that the proposed a-CEP gives better results in terms of accuracy.

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Narayanan, Swathi Jamjala, Rajen B. Bhatt, Ilango Paramasivam, M. Khalid, and B. K. Tripathy. "Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree." International Journal of Advanced Intelligence Paradigms 6, no. 4 (2014): 346. http://dx.doi.org/10.1504/ijaip.2014.066983.

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Marsh, Kevin P. "The Intersection of War and Politics." Armed Forces & Society 38, no. 3 (2011): 413–37. http://dx.doi.org/10.1177/0095327x11415492.

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This study examines the decision-making process of the George W. Bush administration which led to the decision in late 2006 to order the Iraq troop surge. The study analyzes whether the bureaucratic politics model of foreign policy decision making can accurately explain the events of the case. The study seeks to further test the explanatory power and descriptive accuracy of the bureaucratic politics model, while also attaining a more textured, academic understanding of the decision-making process leading to the Iraq troop surge. The decision to order the troop surge in Iraq is one of the more important decisions in post-9/11 U.S. foreign policy and continues to impact U.S. strategy in Iraq, Afghanistan, and overall military doctrine. Finally, the author endeavors to contribute to the further development and refinement of the bureaucratic politics model of foreign policy decision making.

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Haddache, Mohamed, Allel Hadjali, and Hamid Azzoune. "Skyline refinement exploiting fuzzy formal concept analysis." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (2021): 333–62. http://dx.doi.org/10.1108/ijicc-11-2020-0181.

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PurposeThe study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approachThe problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.FindingsAn appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implicationsThe authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.Social implicationsThis work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.Originality/valueThis study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

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Naderi, Sait, Edward C. Benzel, and Nevan G. Baldwin. "Cervical spondylotic myelopathy: surgical decision making." Neurosurgical Focus 1, no. 6 (1996): E3. http://dx.doi.org/10.3171/foc.1996.1.6.4.

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Cervical spondylotic myelopathy can produce a variety of clinical signs and symptoms secondary to neural compromise and biomechanical involvement of the spine. The surgical treatment of cervical spondylotic myelopathy remains a controversial issue after many years of study, evolution, and refinement. Several ventral, dorsal, or combined approaches have been defined. The complications associated with ventral approaches and the concerns about kyphosis following dorsal approaches led to the development of a variety of laminoplasty procedures. This paper reviews the biomechanical basis of cervical spondylotic myelopathy and its effect on choosing the appropriate surgical approach.

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Zhang, Hengshan, Yimin Zhou, Tianhua Chen, Richard Hill, Zhongmin Wang, and Yanping Chen. "Refinement of weights using attribute support for multiple attribute decision making." Journal of Computational Science 54 (September 2021): 101440. http://dx.doi.org/10.1016/j.jocs.2021.101440.

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Chen, Wen-Jan. "Effective soft-decision demosaicking using directional filtering and embedded artifact refinement." Optical Engineering 48, no. 4 (2009): 047003. http://dx.doi.org/10.1117/1.3120284.

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Émond, Marcel, Natalie Le Sage, André Lavoie, and Lynne Moore. "Refinement of the Quebec decision rule for radiography in shoulder dislocation." CJEM 11, no. 01 (2009): 36–43. http://dx.doi.org/10.1017/s1481803500010903.

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ABSTRACTObjective:We prospectively derived a clinical decision rule to guide pre- and postreduction radiography for emergency department (ED) patients with anterior glenohumeral dislocation.Methods:This prospective cohort derivation study took place at 4 university-affiliated EDs over a 3-year period and enrolled consenting patients with anterior glenohumeral dislocation who were 18 years of age or older. We compared patients with a clinically important fracture-dislocation with those who had an uncomplicated dislocation to provide the clinical decision rule components using recursive partitioning. The final rule involved age, mechanism, prior dislocation and humeral ecchymosis.Results:A total of 222 patients were included in the study. Forty (18.0%) had clinically important fracture-dislocation. A clinical decision rule using 4 factors reached a sensitivity of 100% (95% confidence interval [CI] 89.4%–100%), a specificity of 34.2% (95% CI 27.7%–41.2%), a negative predictive value of 99.2% (95% CI 92.8%–99.9%) and a negative likelihood ratio of 0.04 (95% CI 0.002–0.27). Patients younger than 40 years are at high risk for clinically important fracture-dislocation only if the mechanism of injury involves substantial force (i.e., a fall greater than their own height, a sport injury, an assault or a motor vehicle collision). Patients 40 years of age or older are at high risk only in the presence of humeral ecchymosis or after their first dislocation. Projected use of the rule would reduce the absolute number of prereduction radiographs by 27.9% and of postreduction by 81.9%.Conclusion:The Quebec shoulder dislocation rule for patients with acute anterior glenohumeral dislocation holds promise to reduce unnecessary imaging, pending validation.

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Wickliffe, Vanessa Prier. "Refinement and re-assessment of the consumer decision-making style instrument." Journal of Retailing and Consumer Services 11, no. 1 (2004): 9–17. http://dx.doi.org/10.1016/s0969-6989(02)00057-7.

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Zhang, Lin, Xuan Wei huang, and Wei Ming Wu. "The Analysis of Driver's Behavior in Non-Signalized Intersection Based on the Game." Applied Mechanics and Materials 505-506 (January 2014): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1157.

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In order to study the driver's decision-making behaviors of the conflict vehicles in non-signalized intersection, according to time refinement, the driver's personality factors and the relative potential factors in the different strategies which affect the driver to make decisions, Based on the dynamic reduplicate game theory, the utility function of the driver's behaviors was built up. As the decision-making behavior by the driver in the process of cross-road, analyzing the different combination of the utility of the driver's decision-making behavior, Nash equilibrium was existed in a single game process, and the driver's optimal decision behaviors in a dynamic game was obtained. The illustration shows that impulse drivers in the decision-making period of time are more willing to choose to accelerate the first strategy; mild drivers prefer to choose acceleration strategy or uniform strategy; cautious drivers prefer to choose to uniform or deceleration strategy.

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Guo, Jianhong. "Optimization and exploration of reservoir dispatching system based on cloud services." Advances in Engineering Technology Research 10, no. 1 (2024): 7. http://dx.doi.org/10.56028/aetr.10.1.7.2024.

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In response to complex water management needs and extreme climate events, this study explores optimizing reservoir dispatch systems via cloud services, utilizing Huawei's cloud platform. High-precision sensors and edge computing facilitate intelligent dispatch decisions. Simulation experiments informed the tuning of sensor accuracy and data collection frequencies, emphasizing system adaptability and decision-making reliability. The goal is to enhance water resource management, disaster response, ecological security, and economic outcomes through ongoing system refinement.

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Joosten, Robbie P., Krista Joosten, Garib N. Murshudov, and Anastassis Perrakis. "PDB_REDO: constructive validation, more than just looking for errors." Acta Crystallographica Section D Biological Crystallography 68, no. 4 (2012): 484–96. http://dx.doi.org/10.1107/s0907444911054515.

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Developments of the PDB_REDO procedure that combine re-refinement and rebuilding within a unique decision-making framework to improve structures in the PDB are presented. PDB_REDO uses a variety of existing and custom-built software modules to choose an optimal refinement protocol (e.g. anisotropic, isotropic or overall B-factor refinement, TLS model) and to optimize the geometry versus data-refinement weights. Next, it proceeds to rebuild side chains and peptide planes before a final optimization round. PDB_REDO works fully automatically without the need for intervention by a crystallographic expert. The pipeline was tested on 12 000 PDB entries and the great majority of the test cases improved both in terms of crystallographic criteria such as R free and in terms of widely accepted geometric validation criteria. It is concluded that PDB_REDO is useful to update the otherwise `static' structures in the PDB to modern crystallographic standards. The publically available PDB_REDO database provides better model statistics and contributes to better refinement and validation targets.

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Surapaneni, Krishna Mohan, Chandrika Balasubramanian, and Santha Prem Kumar Edison. "Utility of ChatGPT in neuroanesthesia: Still long way to go." Indian Journal of Clinical Anaesthesia 11, no. 3 (2024): 442–44. http://dx.doi.org/10.18231/j.ijca.2024.082.

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With the evolution of Artificial Intelligence (AI), complex and intricate medical decisions are expected to benefit from the insights of such AI-powered large language models like ChatGPT. This study was conducted to examine ChatGPT 3.5's performance in neuroanesthesia through ten clinical cases. While the model shows moderate competency in some respects, it could not generate correct responses for over half of the scenarios underscoring the need for specific training and refinement before practical use in specialized clinical decision support.

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Sobolev, Oleg V. "Detection of alternative conformations:Shift_plotandAC_predictionprograms." Journal of Applied Crystallography 46, no. 2 (2013): 554–59. http://dx.doi.org/10.1107/s0021889812052065.

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Increased mobility of poorly ordered atoms compared to well ordered ones in unrestrained refinement was adopted as the basis for the development of a method for predicting alternative conformations. Two programs for the analysis of atomic mobility are presented.Shift_plotmay be useful for visual analysis of magnitudes of atomic shifts in refinement.AC_predictionimplements automatic decision-making procedures based on atomic shift magnitudes in unrestrained refinement to highlight the specific residues that are likely to harbour alternative conformations and that hence require checkingviaelectron density maps.

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Katschnig, Heinz, Christa Straßmayr, Jose Alberto Salinas-Perez, Mencia R. Gutierrez-Colosia, Luis Salvador-Carulla, and Francesco Amaddeo. "Decision support systems for assessing integrated mental health care: the refinement toolkit." International Journal of Integrated Care 16, no. 6 (2016): 357. http://dx.doi.org/10.5334/ijic.2905.

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Haesaert, Sofie, Sadegh Soudjani, and Alessandro Abate. "Temporal logic control of general Markov decision processes by approximate policy refinement." IFAC-PapersOnLine 51, no. 16 (2018): 73–78. http://dx.doi.org/10.1016/j.ifacol.2018.08.013.

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Kolaitis, Phokion G., Lucian Popa, and Kun Qian. "Knowledge Refinement via Rule Selection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2886–94. http://dx.doi.org/10.1609/aaai.v33i01.33012886.

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In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.

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Zhao, Jane, Klaus-Dieter Schewe, and Henning Köhler. "Dynamic Data Warehouse Design with Abstract State Machines." JUCS - Journal of Universal Computer Science 15, no. (1) (2009): 355–97. https://doi.org/10.3217/jucs-015-01-0355.

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On-line analytical processing (OLAP) systems deal with analytical tasks that support decision making. As these tasks do not depend on the latest updates by transactions, it is assumed that the data required by OLAP systems are kept in a data warehouse, which separates the input from operational databases from the outputs to OLAP. However, user requirements for OLAP systems change over time. Data warehouses and OLAP systems thus are rather dynamic and the design process is continuous. In order to easily incorporate new requirements and at the same time ensure the quality of the system design, we suggest to apply the Abstract State Machine (ASM) based development method. This assumes we capture the basic user requirements in a ground model and then apply stepwise refinements to the ground model for every design decisions or further new requirements. In this article, we show that a systematical approach which is tailored for data warehouse design with a set of formal refinement rules can simplify the work in dynamic data warehouse design and at the same time improves the quality of the system.

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Li, Jinyang, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, and H. V. Jagadish. "Erica: Query Refinement for Diversity Constraint Satisfaction." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4070–73. http://dx.doi.org/10.14778/3611540.3611623.

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Relational queries are commonly used to support decision making in critical domains like hiring and college admissions. For example, a college admissions officer may need to select a subset of the applicants for in-person interviews, who individually meet the qualification requirements (e.g., have a sufficiently high GPA) and are collectively demographically diverse (e.g., include a sufficient number of candidates of each gender and of each race). However, traditional relational queries only support selection conditions checked against each input tuple, and they do not support diversity conditions checked against multiple, possibly overlapping, groups of output tuples. To address this shortcoming, we present Erica, an interactive system that proposes minimal modifications for selection queries to have them satisfy constraints on the cardinalities of multiple groups in the result. We demonstrate the effectiveness of Erica using several real-life datasets and diversity requirements.

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EKENBERG, LOVE, and JOHAN THORBIÖRNSON. "SECOND-ORDER DECISION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, no. 01 (2001): 13–37. http://dx.doi.org/10.1142/s0218488501000582.

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The purpose of this work is to provide theoretical foundations of, as well as some computational aspects on, a theory for analysing decisions under risk, when the available information is vague and imprecise. Many approaches to model unprecise information, e.g., by using interval methods, have prevailed. However, such representation models are unnecessarily restrictive since they do not admit discrimination between beliefs in different values, i.e., the epistemologically possible values have equal weights. In many situations, for instance, when the underlying information results from learning techniques based on variance analyses of statistical data, the expressibility must be extended for a more perceptive treatment of the decision situation. Our contribution herein is an approach for enabling a refinement of the representation model, allowing for an elaborated discrimination of possible values by using belief distributions with weak restrictions. We show how to derive admissible classes of local distributions from sets of global distributions and introduce measures expressing into which extent explicit local distributions can be used for modelling decision situations. As will turn out, this results in a theory that has very attractive features from a computational viewpoint.

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Kouah, Sofia, Djamel Eddine Saïdouni, and Ilham Kitouni. "Open Fuzzy Synchronized Petri Net." International Journal of Intelligent Information Technologies 12, no. 1 (2016): 63–94. http://dx.doi.org/10.4018/ijiit.2016010104.

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Designing Multi agent systems needs a high-level specification model which supports abstraction, dynamicity, openness and enables fuzziness. Since the model of Synchronized Petri Nets supports dynamicity and abstraction, we extend it by fuzziness, openness and interaction with environment. The proposed model called Open Fuzzy Synchronized Petri Nets (OFSyPN for short) associates action name with transitions and enables openness feature and interaction with environment. Each action has an uncertainty degree and places are typed. The authors give an operational semantics for OFSyPN in terms of Fuzzy Labeled Transition System (FLTS for short). FLTS is a semantics model, which allows a concise action refinement representation and deals with incomplete information through its fuzziness representation. Furthermore the structure can be used to produce a tree of potential concurrent design trajectories, named fuzzy labeled transition refinement tree (FLTRT for short). We exemplify the OFSyPN model thought a case study.

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Chalyi, Sergiy F., Volodymyr O. Leshchynskyi, and Iryna O. Leshchynska. "Causal mental model of decision in the task of building explanations in intelligent information system." Management Information System and Devises, no. 183 (December 4, 2024): 82–89. https://doi.org/10.30837/0135-1710.2024.183.082.

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The subject of research is the process of building causal mental models of intelligent system decisions. The aim is to develop a causal approach to building mental models of decisions in artificial intelligence systems to enable the construction and refinement of explanations according to the user's knowledge of the domain. Tasks: structuring mental models considering their properties in the aspect of forming explanations in intelligent systems; development of a causal mental model of intelligent system decision. Conclusions. The analysis of possibilities for using mental models in the task of building explanations has been performed, highlighting causal, analogical, folk, and dynamic models. The scientific novelty of the obtained results is as follows. A causal mental model of intelligent system decision is proposed, which contains a set of alternative representations of causes and effects of the obtained decision, taking into account the possibilities and limitations regarding the use of the decision. The model takes into account both positive and negative properties of the obtained result, which creates conditions for increasing the level of user trust. A formal representation of the causal mental model has been developed, containing temporally ordered rules that link input data as causes and properties of the obtained decision as effects. The rules are combined into alternatives, which makes it possible to select explanations according to input data constraints and limitations on the possibilities of using the intelligent system decision.

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Aloini, Davide, Riccardo Dulmin, Valeria Mininno, and Pierluigi Zerbino. "Leveraging procurement-related knowledge through a fuzzy-based DSS: a refinement of purchasing portfolio models." Journal of Knowledge Management 23, no. 6 (2019): 1077–104. http://dx.doi.org/10.1108/jkm-10-2018-0614.

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Purpose This paper aims to model a decision support system (DSS) that could overcome the oversimplified, subjective, compensatory decision logic of extant purchasing portfolio models (PPMs) by leveraging the firms’ procurement-related knowledge base. Design/methodology/approach The DSS was developed through a fuzzy-based approach, whose design and application were framed within a case study in a multinational company. Findings The application of the fuzzy-based DSS to a product class suggests investing in the relationship with two specific suppliers and to loosen the relationship with a third one. Research limitations/implications Exploiting the fuzzy set theory and fostering the elicitation of procurement-related knowledge from the decision-makers, the DSS effectively tackles the concerns about the existing PPMs by including strategic-oriented priorities and contextual constraints in the evaluation. Practical implications The recommendations in output from the DSS are feasible, more analytical and easy to interpret, enabling knowledge sharing, group decision processes and better decision-making. Originality/value To the best of the authors’ knowledge, this manuscript is the first attempt to effectively integrate traditional PPMs with contextual, strategy-related factors to refine the purchasing directions and make them objective.

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Lee, Ruby. "Future Care Planning with Families: Mental Capacity, Deputyship, and Its Implications." Singapore Family Physician 48, no. 6 (2022): 41–44. http://dx.doi.org/10.33591/sfp.48.6.u6.

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This article is intended to give an overview of the current state of the MCA and provide some thoughts on how deputyship works under the MCA framework. The article discusses the different categories of proxy decision makers and some of the limitations of proxy decision-making. It also seeks to stimulate some thoughts about the refinement of the current state of care for persons with no or reduced mental capacity.

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Abhinay Sama. "The echo of human bias in AI refinement." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2680–87. https://doi.org/10.30574/wjaets.2025.15.2.0819.

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The Echo of Human Bias in AI Refinement explores how human prejudices infiltrate Artificial Intelligence systems throughout their development lifecycle. From initial training data embedded with societal inequalities to refinement processes that encode evaluator preferences, bias enters AI through multiple channels. The article traces this journey through four stages: data collection, human feedback mechanisms, fine-tuning processes, and iterative development. Real-world consequences manifest in financial services, navigation systems, and healthcare, where algorithmic decision-making can amplify existing disparities. Mitigation strategies include implementing rigorous bias detection throughout development, diversifying data and feedback sources, establishing transparent human oversight, and fostering interdisciplinary collaboration. By understanding these mechanisms, we can develop AI systems that better serve all of humanity rather than perpetuating historical inequities.

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Brucker, Achim, Frank Rittinger, and Burkhart Wolff. "HOL-Z 2.0: A Proof Environment for Z-Specifications." JUCS - Journal of Universal Computer Science 9, no. (2) (2003): 152–72. https://doi.org/10.3217/jucs-009-02-0152.

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We present a new proof environment for the specification language Z. The basis is a semantic representation of Z in a structure-preserving, shallow embedding in Isabelle/HOL. On top of the embedding, new proof support for the Z schema calculus and for proof structuring are developed. Thus, we integrate Z into a well-known and trusted theorem prover with advanced deduction technology such as higher-order rewriting, tableaux-based provers and arithmetic decision procedures. A further achievement of this work is the integration of our embedding into a new tool-chain providing a Z-oriented type checker, documentation facilities and macro support for refinement proofs, as a result, the gap has been closed between a logical embedding proven correct and a tool suited for applications of nontrivial size.

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Mei, Qiongzhen, and Fei Wang. "Robust Refinement of Built-in Network Information System Based on Nonparametric Density Estimation." Journal of Sensors 2022 (May 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/4220001.

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In the research process of robustness refinement solution of built-in information systems for electronic networks, there are too many factors related to robustness in the current system robustness design, and different factors have different influences on robustness. In stochastic programming problems, uncertain variables usually obey a certain probability distribution, but in real decision-making, these determined distributions are often unknown or we only know part of the information of the distributions, and distributed robust refinement solution is just an effective solution to solve uncertain problems. The robustness measurement solution of information systems for electronic networks is analyzed. The robust refinement of information system is deeply studied by nonparametric density estimation solution, which is based on the strict robustness requirements put forward by users. Based on the research results of interdependent network theory and aiming at “improving the robustness of electronic information system,” this paper makes an in-depth study on the robustness refinement strategy of power information system. The comparison between the companies that adopted the robust refinement of built-in information systems for electronic networks based on nonparametric density estimation and the companies that did not adopt it shows that the refinement rate of the companies that adopted it in the first three years was 82%, while that of the companies that did not adopt it was only 57%, and the overall misjudgment rate was 43%. Therefore, it is proved that using the proposed reliability refinement solution to optimize, the embedded system can improve the service life, modeling accuracy, and availability of the system and has certain practicability.

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Haesaert, Sofie, Sadegh Esmaeil Zadeh Soudjani, and Alessandro Abate. "Verification of General Markov Decision Processes by Approximate Similarity Relations and Policy Refinement." SIAM Journal on Control and Optimization 55, no. 4 (2017): 2333–67. http://dx.doi.org/10.1137/16m1079397.

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Deng, Pi-Sheng. "Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model." Decision Sciences 24, no. 2 (1993): 371–94. http://dx.doi.org/10.1111/j.1540-5915.1993.tb00479.x.

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Tsujino, Katsuhiko, and Shogo Nishida. "Implementation and refinement of decision trees using neural networks for hybrid knowledge acquisition." Artificial Intelligence in Engineering 9, no. 4 (1995): 265–76. http://dx.doi.org/10.1016/0954-1810(95)00005-4.

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Li, Bing, Chao Wang, and Fabio Somenzi. "Abstraction refinement in symbolic model checking using satisfiability as the only decision procedure." International Journal on Software Tools for Technology Transfer 7, no. 2 (2005): 143–55. http://dx.doi.org/10.1007/s10009-004-0169-2.

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Wood, William H., and Alice M. Agogino. "Decision-Based Conceptual Design: Modeling and Navigating Heterogeneous Design Spaces." Journal of Mechanical Design 127, no. 1 (2005): 2–11. http://dx.doi.org/10.1115/1.1799612.

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Information gathering and refinement are critical activities in conceptual design. A decision-based framework is developed consisting of three main components: a flexible, extensible design space model based on a Gaussian kernel which synthesizes information from design instances; expected value decision-making which focuses the design process on the most promising subspaces within this model; and information value theory which identifies uncertainty in the design evaluation whose reduction could redirect the design process. Together, these components form a normative method for conceptual design around a key process—the co-evolution of a design and the evaluation model used to quantify its value. Formalizing conceptual design toward reducing arbitrary design decisions and focusing attention on the most critical design concerns holds the potential to substantially improve both the process and product of design. The proposed methodology is demonstrated through an example in the domain of electric motor selection.

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Parimi, Viraj, Sungkweon Hong, and Brian Williams. "Task-driven Risk-bounded Hierarchical Reinforcement Learning Based on Iterative Refinement." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 573–75. http://dx.doi.org/10.1609/aaaiss.v3i1.31281.

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Deep Reinforcement Learning (DRL) has garnered substantial acclaim for its versatility and widespread applications across diverse domains. Aligned with human-like learning, DRL is grounded in the fundamental principle of learning from interaction, wherein agents dynamically adjust behavior based on environmental feedback in the form of rewards. This iterative trial-and-error process, mirroring human learning, underscores the importance of observation, experimentation, and feedback in shaping understanding and behavior. DRL agents, trained to navigate complex surroundings, refine their knowledge through hierarchical and abstract representations, empowered by deep neural networks. These representations enable efficient handling of long-horizon tasks and flexible adaptation to novel situations, akin to the human ability to construct mental models for comprehending complex concepts and predicting outcomes. Hence, abstract representation building emerges as a critical aspect in the learning processes of both artificial agents and human learners, particularly in long-horizon tasks. Furthermore, human decision-making, deeply rooted in evolutionary history, exhibits a remarkable capacity to balance the tradeoff between risk and cost across various domains. This cognitive process involves assessing potential negative consequences, evaluating factors such as the likelihood of adverse outcomes, severity of potential harm, and overall uncertainty. Humans intuitively gauge inherent risks and adeptly weigh associated costs, extending beyond monetary expenses to include time, effort, and opportunity costs. The nuanced ability of humans to consider the tradeoff between risk and cost highlights the complexity and adaptability of human decision-making, a skill lacking in typical DRL agents. Principles like these derived from human-like learning present an avenue for inspiring advancements in DRL, fostering the development of more adaptive and intelligent artificial agents. Motivated by these observations and focusing on practical challenges in robotics, our efforts target risk-aware stochastic sequential decision-making problem which is crucial for tasks with extended time frames and varied strategies. A novel integration of model-based conditional planning with DRL is proposed, inspired by hierarchical techniques. This approach breaks down complex tasks into manageable subtasks(motion primitives), ensuring safety constraints and informed decision-making. Unlike existing methods, our approach addresses motion primitive improvement iteratively, employing diverse prioritization functions to guide the search process effectively. This risk-bounded planning algorithm seamlessly integrates conditional planning and motion primitive learning, prioritizing computational efforts for enhanced efficiency within specified time limits.

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ZHAO, YAN, YIYU YAO, and JINGTAO YAO. "LEVEL-WISE CONSTRUCTION OF DECISION TREES FOR CLASSIFICATION." International Journal of Software Engineering and Knowledge Engineering 16, no. 01 (2006): 103–26. http://dx.doi.org/10.1142/s0218194006002690.

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A partition-based framework is presented for a formal study of classification problems. An information table is used as a knowledge representation, in which all basic notions are precisely defined by using a language known as the decision logic language. Solutions to, and solution space of, classification problems are formulated in terms of partitions. Algorithms for finding solutions are modelled as searching in a space of partitions under the refinement order relation. We focus on a particular type of solutions called conjunctively definable partitions. Two level-wise methods for decision tree construction are investigated, which are related to two different strategies: local optimization and global optimization. They are not in competition with, but are complementary to each other. Experimental results are reported to evaluate the two methods.

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Ramakrishna, Y. S., L. E. Moser, L. K. Dillon, P. M. Melliar-Smith, and G. Kutty. "An Automata-Theoretic Decision Procedure for Propositional Temporal Logic with Since and Until1." Fundamenta Informaticae 17, no. 3 (1992): 271–82. http://dx.doi.org/10.3233/fi-1992-17307.

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We present an automata-theoretic decision procedure for Since/Until Temporal Logic (SUTL), a linear-time propositional temporal logic with strong non-strict since and until operators. The logic, which is intended for specifying and reasoning about computer systems, employs neither next nor previous operators. Such operators obstruct the use of hierarchical abstraction and refinement and make reasoning about concurrency difficult. A proof of the soundness and completeness of the decision procedure is given, and its complexity is analyzed.

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Drent, Ailsa, and Erica Frydenberg. "Teaching decision making in schools. How well does it work?" Journal of Psychologists and Counsellors in Schools 5 (November 1995): 87–98. http://dx.doi.org/10.1017/s1037291100001758.

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Considerable interest has been shown in the teaching of decision-making skills to Australian adolescents during the past decade. Acceptable modes of teaching are currently being researched and refined. The experiences of one Victorian co-educational school as it grapples with this task are described. How the program is adapted and modified over a three-year period is detailed. Some encouraging findings in relation to teaching methodology, ‘at risk’ students and parent partnership are described, as well as future suggestions for program refinement. The value of multi-model evaluation is discussed as it relates to the various facets of the program.

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Song, Cai. "Educational Information Refinement with Application Using Massive-Scale Data Mining." Mathematical Problems in Engineering 2022 (April 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/2372723.

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In the manuscript, we propose a novel online learning mechanism based on educational data mining. By leveraging the computer-assisted information-based learning guidance platform, we collect the relevant information of students’ login platform and resource browsing. Subsequently, we preprocess these students’ login data based on which the statistical analysis of students’ login and resource browsing habits are learned through a decision-making mechanism. The decision tree algorithm discovers the underlying factors that influence it from million-scale real-world instructors/students. In this way, instructors and teaching staff can effectively grasp the learning process of students according to the analyzing results. Based on this, the target teaching content integration and teaching model construction can be realized accordingly. This can substantially improve the effectiveness and quality of online learning. In the evaluation stage, we observe that the strategy to deploy a virtual lab environment vigorously brings greater flexibility in the allocation of computing resources to educational institutions. In an ideal sandboxed laboratory context, students can obtain and create an internal network and then have accession to all the computers conveniently. By doing so, gathering savvy skills gives them the workability to build architectures based on mined data. In this work, we adopt the so-called EDUCloud to provide a personal cloud network connection that can flexibly deploy the mined laboratory-related data to facilitate online learning between instructors and students.

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Kapoor, Ashish, Bongshin Lee, Desney Tan, and Eric Horvitz. "Performance and Preferences: Interactive Refinement of Machine Learning Procedures." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1578–84. http://dx.doi.org/10.1609/aaai.v26i1.8340.

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Problem-solving procedures have been typically aimed at achieving well-defined goals or satisfying straightforward preferences. However, learners and solvers may often generate rich multiattribute results with procedures guided by sets of controls that define different dimensions of quality. We explore methods that enable people to explore and express preferences about the operation of classification models in supervised multiclass learning. We leverage a leave-one-out confusion matrix that provides users with views and real-time controls of a model space. The approach allows people to consider in an interactive manner the global implications of local changes in decision boundaries. We focus on kernel classifiers and show the effectiveness of the methodology on a variety of tasks.

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