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Articles de revues sur le sujet « Predictive Reasoning »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Stauffer, E. Shannon. "HIGH TECH VS PREDICTIVE REASONING." Orthopedics 18, no. 10 (1995): 967. http://dx.doi.org/10.3928/0147-7447-19951001-04.

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Oslington, Gabrielle, Joanne Mulligan, and Penny Van Bergen. "Third-graders’ predictive reasoning strategies." Educational Studies in Mathematics 104, no. 1 (2020): 5–24. http://dx.doi.org/10.1007/s10649-020-09949-0.

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Białek, Michał. "Język, logika a rozumowanie – teoria reguł czy modele umysłowe?" Acta Universitatis Lodziensis. Folia Psychologica, no. 12 (January 1, 2008): 45–54. http://dx.doi.org/10.18778/1427-969x.12.03.

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In present research there was shown the issue of connection between reasoning and logical competence. The point of view of rules theory and mental models theory concerning discussed range of problems was presented and subsequently the prediction of rules theory was verified. The results of the survey conducted on 70 people: 37 students of philosophy and 33 students of other courses at University of Lodz let us reject rules theory as a predictive to the proceedings of reasoning process and induce to accept an alternative theory explaining the proceedings of the discussed process. The adduced re

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Li, Yi, Hridya Dhulipala, Aashish Yadavally, Xiaokai Rong, Shaohua Wang, and Tien N. Nguyen. "Blended Analysis for Predictive Execution." Proceedings of the ACM on Software Engineering 2, FSE (2025): 2987–3008. https://doi.org/10.1145/3729402.

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Although Large Language Models (LLMs) are highly proficient in understanding source code and descriptive texts, they have limitations in reasoning on dynamic program behaviors, such as execution trace and code coverage prediction, and runtime error prediction, which usually require actual program execution. To advance the ability of LLMs in predicting dynamic behaviors, we leverage the strengths of both approaches, Program Analysis (PA) and LLM, in building PredEx, a predictive executor for Python. Our principle is a blended analysis between PA and LLM to use PA to guide the LLM in predicting

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Fernbach, Philip M., Adam Darlow, and Steven A. Sloman. "Asymmetries in predictive and diagnostic reasoning." Journal of Experimental Psychology: General 140, no. 2 (2011): 168–85. http://dx.doi.org/10.1037/a0022100.

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Rodrigo, María J., Manuel de Vega, and Javier Castaneda. "Updating mental models in predictive reasoning." European Journal of Cognitive Psychology 4, no. 2 (1992): 141–57. http://dx.doi.org/10.1080/09541449208406247.

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Lim, Tow Keang. "The predictive brain model in diagnostic reasoning." Asia Pacific Scholar 6, no. 2 (2021): 1–8. http://dx.doi.org/10.29060/taps.2021-6-2/ra2370.

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Introduction: Clinical diagnosis is a pivotal and highly valued skill in medical practice. Most current interventions for teaching and improving diagnostic reasoning are based on the dual process model of cognition. Recent studies which have applied the popular dual process model to improve diagnostic performance by “Cognitive De-biasing” in clinicians have yielded disappointing results. Thus, it may be appropriate to also consider alternative models of cognitive processing in the teaching and practice of clinical reasoning. Methods: This is critical-narrative review of the predictive brain mo

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Lathifaturrahmah, Lathifaturrahmah, Toto Nusantara, Subanji Subanji, and Makbul Muksar. "Predictive reasoning of senior high school students in handling COVID-19 data." Eurasia Journal of Mathematics, Science and Technology Education 19, no. 4 (2023): em2253. http://dx.doi.org/10.29333/ejmste/13110.

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The purpose of this study is to describe the characteristics of predictive reasoning made by students in solving graph-related problems, particularly related to COVID-19. This is a descriptive qualitative study with data collected from a sample size of 25 senior high school students and analyzed using the <i>generalization-prediction task</i>. The result revealed that there are three types of students’ predictive reasoning made based on (1) data observation, (2) data observation coupled with prior experience, and (3) data observation coupled with prior experience or knowledge. The

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Yuan, Ye, Zhong Kai Yang, and Qing Fu Li. "End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning." Applied Mechanics and Materials 55-57 (May 2011): 407–12. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.407.

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This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring.

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Wang, W. C. "Personalized Prediction Model for Hepatocellular Carcinoma With a Bayesian Clinical Reasoning Approach." Journal of Global Oncology 4, Supplement 2 (2018): 210s. http://dx.doi.org/10.1200/jgo.18.84600.

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Background: Predictive models for the risk of hepatocellular carcinoma (HCC) are often appropriate for average-risk population but not tailored for a personalized prediction model for individual risk of hepatocellular carcinoma (HCC), namely personalized prediction model. Aim: The objective of this study is to build up an individually tailored predictive model for HCC by using a Bayesian clinical reasoning algorithm to stratify risk groups of the underlying population. Methods: Data were derived from a community-based screening cohort consisting of 98,552 subjects between 1999 and 2007. Inform

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Habeck, Christian, Qolamreza Razlighi, and Yaakov Stern. "Predictive utility of task-related functional connectivity vs. voxel activation." PLOS ONE 16, no. 4 (2021): e0249947. http://dx.doi.org/10.1371/journal.pone.0249947.

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Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20–77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during per

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Legaspi, Roberto, Raymund Sison, Ken-ichi Fukui, and Masayuki Numao. "Cluster-based predictive modeling to improve pedagogic reasoning." Computers in Human Behavior 24, no. 2 (2008): 153–72. http://dx.doi.org/10.1016/j.chb.2007.01.007.

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Armbruster, Jonas, Eva Steinhausen, Simon Hackl, et al. "The Power of Heuristics in Predicting Fracture Nonunion." Journal of Clinical Medicine 14, no. 8 (2025): 2713. https://doi.org/10.3390/jcm14082713.

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Background/Objectives: Although extensive research on risk factors for nonunion development has been published, clinicians frequently rely on heuristic reasoning—intuitive, experience-based decision-making—to predict nonunions. However, the accuracy of these intuitive assessments and the influence of clinician experience remain uncertain. This study aims to assess clinicians’ diagnostic accuracy in predicting nonunion, investigate the impact of experience on predictive performance, and identify patient-specific factors contributing to diagnostic errors. Methods: This retrospective, multi-cente

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Roccetti, Marco. "Predictive health intelligence: Potential, limitations and sense making." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10459–63. http://dx.doi.org/10.3934/mbe.2023460.

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<abstract> <p>We discuss the new paradigm of <italic>predictive health intelligence</italic>, based on the use of modern deep learning algorithms and big biomedical data, along the various dimensions of: a) its potential, b) the limitations it encounters, and c) the sense it makes. We conclude by reasoning on the idea that viewing data as the unique source of sanitary knowledge, fully abstracting from human medical reasoning, may affect the scientific credibility of health predictions.</p> </abstract>

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Józsa, Krisztián, Tun Zaw Oo, Diana Borbélyová, and Judit Podráczky. "Deductive Reasoning Skills in Children Aged 4–8 Years Old." Journal of Intelligence 12, no. 3 (2024): 33. http://dx.doi.org/10.3390/jintelligence12030033.

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Young children possess the fundamental deductive reasoning skills for solving their upcoming problems in their daily lives. These skills are of great importance for their school readiness and academic development. Therefore, this study aimed to explore the age differences and predictive variables of deductive reasoning skills in young Hungarian children aged 4–8 years old who reside in Hungary and Slovakia. Face-to-face data were collected from 3050 participants. The instrument of deductive reasoning skills assessment was extracted from the school readiness test, DIFER (Diagnostic System for A

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Chapdelaine, Nadine, Erika-Lyne Smith, and Nathalie Poirier. "Liens et valeurs prédictives du raisonnement fluide et des fonctions exécutives sur les habiletés en mathématiques d’élèves québécois ayant un trouble du spectre de l’autisme." Annales de didactique et de sciences cognitives 29 (2024): 9–29. https://doi.org/10.4000/12ym0.

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and restricted/repetitive patterns of behaviours. Mathematical abilities can be influenced by executive functions and fluent reasoning. This study describes the mathematical skills of 20 Quebec students with ASD and the predictive values of fluent reasoning and executive functions on these skills. Results reveal overall mathematics scores within the low average range. Fluid reasoning and executive functions are significantly correlated with mathematical skills. According to the reg

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Williams, Patricia Couch, R. Steve McCallum, and Mellissa Testerman Reed. "Predictive Validity of the Cattell-Horn Gf-Gc Constructs to Achievement." Assessment 3, no. 1 (1996): 43–51. http://dx.doi.org/10.1177/107319119600300105.

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The predictive validity of cognitive constructs taken from Cattell-Horn's Gf-Gc Model was examined. Gf-Gc cognitive constructs were measured using the Woodcock-Johnson-Revised Tests of Cognitive Ability; they include processing speed, fluid reasoning, acculturation-knowledge, short-term memory, long-term retrieval, auditory processing, and visual processing. Scores from the Comprehensive Test of Basic Skills were used as the criterion measures for 104 elementary, middle, and high school students. Using multiple regression equations, various combinations of the Comprehension-Knowledge, Fluid Re

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Sakuragi, Minoru, Eiichiro Uchino, Noriaki Sato, et al. "Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy." PLOS ONE 19, no. 3 (2024): e0298673. http://dx.doi.org/10.1371/journal.pone.0298673.

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Background Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach w

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Aßfalg, André, and Karl Christoph Klauer. "Reasoners consider alternative causes in predictive and diagnostic reasoning." Journal of Experimental Psychology: Learning, Memory, and Cognition 45, no. 12 (2019): 2188–208. http://dx.doi.org/10.1037/xlm0000706.

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Bulut, Ahsen Seda, and Okan Kuzu. "Mathematical Reasoning Skills as a Predictive of Number Sense." International Journal of Progressive Education 19, no. 5 (2023): 172–85. http://dx.doi.org/10.29329/ijpe.2023.603.11.

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Liu, Weitong, Khairunnisa Hasikin, Anis Salwa Mohd Khairuddin, Meizhen Liu, and Xuechen Zhao. "A temporal knowledge graph reasoning model based on recurrent encoding and contrastive learning." PeerJ Computer Science 11 (January 23, 2025): e2595. https://doi.org/10.7717/peerj-cs.2595.

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Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is of great significance and presents considerable obstacles. While current models consider the fact changes over time and recognize that historical facts may recur, they often overlook the influence of past events on future predictions. Motivated by these considerations, this work introduces a novel

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PARSONS, SIMON. "FURTHER RESULTS IN QUALITATIVE UNCERTAINTY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 02 (1995): 187–210. http://dx.doi.org/10.1142/s0218488595000141.

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This paper extends previous work on propagating qualitative uncertainty in networks in which a general approach to qualitative propagation was discussed. The work presented here includes results that make it possible to perform evidential and intercausal reasoning, in addition to the predictive reasoning already covered, in networks quantified with probability, possibility and Dempster-Shafer belief values. The use of these forms of reasoning, which include the phenomenon of “explaining away”, is illustrated with the use of a medical example.

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Gomes, Cristiano Mauro Assis, Jhonys De Araujo, and Enio Galinkin Jelihovschi. "Predictive and incremental validity of Students’ Learning Approach Test (SLAT-Thinking)." Revista Interamericana de Psicología/Interamerican Journal of Psychology 57, no. 3 (2023): e1514. http://dx.doi.org/10.30849/ripijp.v57i3.1514.

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SLAT-Thinking is the only test that evaluates and distinguishes stages of approaches through performance. Although SLAT-Thinking shows evidence of internal validity, its external validity has not yet been examined. In this paper we study the predictive and incremental validities of SLAT-Thinking. Two models were tested. The predictors were inductive reasoning, SLAT-Thinking approaches and Learning Approaches Scale (EABAP) approaches. The outcome was the Brazilian large-scale exam that evaluates the students that finish secondary education. In both models, the superficial approach of SLAT-Think

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Kaufmann, Mareile, Simon Egbert, and Matthias Leese. "Predictive Policing and the Politics of Patterns." British Journal of Criminology 59, no. 3 (2018): 674–92. http://dx.doi.org/10.1093/bjc/azy060.

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AbstractPatterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different mo

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Hildebrandt, Marcel, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, and Volker Tresp. "Reasoning on Knowledge Graphs with Debate Dynamics." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4123–31. http://dx.doi.org/10.1609/aaai.v34i04.6600.

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We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments – paths in the knowledge graph – with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for

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Michalopoulou, Anastasia, and Sonia Kafoussi. "Investigating statistical predictions with first graders in Greece." International Electronic Journal of Mathematics Education 19, no. 2 (2024): em0771. http://dx.doi.org/10.29333/iejme/14247.

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This paper argues that engaging students in informal statistical reasoning from early school years is essential for the development of statistical understanding. We investigated if and how children aged six-seven years old identified variation in a table of data and made predictions through the design of a teaching experiment. The classroom teaching experiment was comprised of four 45 minutes lessons addressing the understanding and interpretation of data sets. In order to describe students’ informal predictive reasoning, we used the framework of “data lenses”. More specifically, we analyzed t

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Bell, J. "Natural Events." Journal of Artificial Intelligence Research 30 (November 6, 2007): 361–412. http://dx.doi.org/10.1613/jair.2383.

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This paper develops an inductive theory of predictive common sense reasoning. The theory provides the basis for an integrated solution to the three traditional problems of reasoning about change; the frame, qualification, and ramification problems. The theory is also capable of representing non-deterministic events, and it provides a means for stating defeasible preferences over the outcomes of conflicting simultaneous events.

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Lucifora, Chiara, Gabriella Martino, Anna Curcuruto, Mohammad Ali Salehinejad, and Carmelo Mario Vicario. "How Self-Control Predicts Moral Decision Making: An Exploratory Study on Healthy Participants." International Journal of Environmental Research and Public Health 18, no. 7 (2021): 3840. http://dx.doi.org/10.3390/ijerph18073840.

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Research on moral reasoning calls into question self-control, which encompasses impulsivity, compulsivity, and inhibitory control. However, a thorough investigation exploring how these three dimensions can affect moral reasoning in response to different scenarios is unavailable. We addressed this topic by testing the predictive role of these three dimensions of self-control on appraisals for ethical violations related with different types of scenarios. Overall, our results suggest that all three dimensions of self-control are involved in moral reasoning, depending on the type of appraisal and

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Minn, Sein, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima, and Feida Zhu. "Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12810–18. http://dx.doi.org/10.1609/aaai.v36i11.21560.

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Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful

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Schult, Johannes, Franziska T. Fischer, and Benedikt Hell. "Tests of Scholastic Aptitude Cover Reasoning Facets Sufficiently." European Journal of Psychological Assessment 32, no. 3 (2016): 215–19. http://dx.doi.org/10.1027/1015-5759/a000247.

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Abstract. This study explores how reasoning facets relate to tests of scholastic aptitude and to academic performance. Intelligence test scores and academic aptitude test scores from freshman students in science (n = 284) and economics (n = 359) as well as subsequent grades from their first year in college were used to analyze structural equation models. The direct influence of reasoning facets on academic performance is fully mediated by academic aptitude test scores. Numeric abilities dominate the aptitude tests’ predictive power. Verbal reasoning explains a significant amount of aptitude te

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Dvoinin, A. M., and E. S. Trotskaya. "Cognitive Predictors of Academic Success: How Do the General Patterns Work in the Early Stages of Education?" Психологическая наука и образование 27, no. 2 (2022): 42–52. http://dx.doi.org/10.17759/pse.2022270204.

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The article provides an overview of modern works devoted to the study of cognitive predictors of academic success. The general patterns of forecasting are revealed: the most powerful and universal predictor of academic success at different stages of school education is psychometric intelligence; creativity is less significant and rather unstable. It is argued that these patterns are poorly traced at the level of preschool education. Particular cognitive functions are significant for predicting the future educational achievements of preschoolers: information processing speed, visual perception

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Wang, Sujuan, and Musadaq Mansoor. "Optimization model for enterprise financial management utilizing genetic algorithms and fuzzy logic." PeerJ Computer Science 11 (April 7, 2025): e2812. https://doi.org/10.7717/peerj-cs.2812.

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This study explores the complexities of enterprise financial management by optimizing financial models with a particular focus on enhancing risk prediction performance. A multi-objective mathematical model is first developed to establish key optimization goals, including cost reduction, improved capital utilization, and increased economic benefits. This model systematically defines decision variables and optimization objectives, providing a comprehensive framework for enterprise financial management. To improve predictive accuracy, the study integrates genetic algorithms with back-propagation

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Morasse, Frédérick, Annie Bernier, Gabrielle Lalonde, Sébastien Hétu, and Miriam H. Beauchamp. "Sociomoral Reasoning Skills during Childhood: A Comprehensive and Predictive Approach." Brain Sciences 12, no. 9 (2022): 1226. http://dx.doi.org/10.3390/brainsci12091226.

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Sociomoral reasoning (SMR) is an essential component of social functioning allowing children to establish judgments based on moral criteria. The progressive emergence and complexification of SMR during childhood is thought to be underpinned by a range of characteristics and abilities present in the preschool years. Past studies have mostly examined concurrent associations between individual factors and SMR. Using a more comprehensive and predictive approach to identify early predictors of school-age SMR would contribute to a more complete picture of SMR development. This study aimed to investi

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Fernbach, Philip M., Adam Darlow, and Steven A. Sloman. "Neglect of Alternative Causes in Predictive but Not Diagnostic Reasoning." Psychological Science 21, no. 3 (2010): 329–36. http://dx.doi.org/10.1177/0956797610361430.

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Coles, Matthew, Djamel Azzi, and Barry Haynes. "A self‐healing mobile wireless sensor network using predictive reasoning." Sensor Review 28, no. 4 (2008): 326–33. http://dx.doi.org/10.1108/02602280810902631.

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Anagnostopoulos, Christos, and Kostas Kolomvatsos. "Predictive intelligence to the edge through approximate collaborative context reasoning." Applied Intelligence 48, no. 4 (2017): 966–91. http://dx.doi.org/10.1007/s10489-017-1032-y.

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Beden, Sadeer, Kayal Lakshmanan, Cinzia Giannetti, and Arnold Beckmann. "Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning." Applied Sciences 13, no. 23 (2023): 12778. http://dx.doi.org/10.3390/app132312778.

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This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with ex

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Aravind, Ravi, and Chirag Vinalbhai Shah. "Physics Model-Based Design for Predictive Maintenance in Autonomous Vehicles Using AI." International Journal of Scientific Research and Management (IJSRM) 11, no. 09 (2023): 932–46. http://dx.doi.org/10.18535/ijsrm/v11i09.ec06.

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In this paper, a model of predictive auto-maintenance is developed. This model is then used for auto-maintenance in the electrical and electronic systems of an autonomous vehicle. Along with traditional sensors, an advanced X-ray vision system is used to detect faults in the system. It uses massively scaled integration (MSI) or very large-scale integration (VLSI) integrated circuitry and other designs to enable the creation of systems containing many components. Commercial applications are being researched and developed, especially in robotics. In this paper, reconfigurable devices are employe

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Rochberg, Francesca. "Reasoning, Representing, and Modeling in Babylonian Astronomy." Journal of Ancient Near Eastern History 5, no. 1-2 (2018): 131–47. http://dx.doi.org/10.1515/janeh-2018-0009.

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AbstractThis paper considers Babylonian astronomical predictive schemes as a source for the study of reasoning and representing via modeling. Two principal questions are addressed: first, whether Babylonian astronomical modeling can be usefully included in the conversation about scientific reasoning with models, and second, how and what the representational value of the practice of astronomical modeling was in ancient Babylonia. It is found that the Babylonian astronomical schemes demonstrate the adaptability and various capabilities of the process of modeling as a powerful tool of representat

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Gutu, Rose, Lameck Mugwagwa, and Nicholas Tayisepi. "PREDICTING INJECTION MOULDED PLASTIC FITTINGS QUALITY USING LARGE LANGUAGE MODELS." Platform A Journal of Science and Technology 8, no. 1 (2025): 1. https://doi.org/10.61762/pjstvol8iss1art001.

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Maintaining consistent quality in plastic injection moulding is crucial for product functionality and profitability. Traditional quality control methods often rely on manual inspections, are prone to errors and inefficiency, and result in defects and financial losses. This research explores the potential of large language models (LLMs) for real-time, data-driven quality assessments in plastic injection moulding. A Taguchi experiment design was conducted to optimise prompting techniques for an LLM, focusing on the accuracy, reliability, and interpretability of predictions. The experiments syste

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White, Stephen J. "INTENTION AND PREDICTION IN MEANS-END REASONING." American Philosophical Quarterly 55, no. 3 (2018): 251–66. http://dx.doi.org/10.2307/45128619.

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Abstract How, if at all, does one’s intention to realize an end bear on the justification for taking the means to that end? Theories that allow that intending an end directly provides a reason to take the means are subject to a well-known "bootstrapping" objection. On the other hand, "anti-psychologistic" accounts—which seek to derive instrumental reasons directly from the reasons that support adopting the end itself—have unacceptable implications where an agent faces multiple rationally permissible options. An alternative, predictive, role for intention in means-end reasoning is considered an

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Hadiati, Soka, Dwi Fajar Saputri, Wahyudi Wahyudi, and Adi Pramuda. "Model-Based Reasoning in the Deductive Laboratory Work with Arduino and Sensor Module." Berkala Ilmiah Pendidikan Fisika 9, no. 3 (2021): 266. http://dx.doi.org/10.20527/bipf.v9i3.8735.

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Laboratory work that has been applied so far is more dominant in deductive laboratory work, but this type of laboratory work is not optimal in stimulating students' reasoning ability. Up to now, model-based reasoning studies on laboratory work have been substantial (especially in measurement and troubleshooting activities) and have not seen overall laboratory activity, including the use of alternative instrumentation such as Arduino and its sensor module. Designing the best laboratory work requires the role of model-based reasoning studies using Arduino devices. A mixed-method research design

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Flexer, Michael J. "If p0, then 1: The impossibility of thinking out cases." History of the Human Sciences 33, no. 3-4 (2020): 175–97. http://dx.doi.org/10.1177/0952695120944032.

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Forrester’s proposed seventh style of reasoning – thinking in cases – functions as an analogous, dyadic relationship that, whilst indebted philosophically to the logical reasoning and semiotics of Charles Peirce, is prone to creating feedback loops between induction and deduction, precluding novel abductive hypotheses from advancing medical knowledge. Reasoning with a Peircean triadic model opens up the contexts and methods of meaning-making and reasoning through medical cases, and the potent influence of their genre conventions, to intellectual critical scrutiny. Vitally, it offers a third mo

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Zhang, Guoliang, Peng Zhang, Fei Zhou, et al. "Research on the generation of localized overheating samples and fault prediction models of transformers and based on brain-inspired spiking neural networks." International Journal of Low-Carbon Technologies 20 (2025): 436–42. https://doi.org/10.1093/ijlct/ctae294.

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Abstract The prediction of transformer failures holds significant importance for maintaining the stability of power systems. This paper investigates the application of a brain-inspired spiking neural network model for fault prediction of transformers. The research is grounded in dissolved gas analysis, employing fuzzy reasoning spiking neural P systems to process fuzzy diagnostic knowledge. It constructs a set of sample data associated with localized overheating and utilizes linguistic variables, membership functions, and an inference rule base to conduct fault analysis. The results indicate t

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Kosaraju, Deekshitha. "Causal Inference and AI: Elevating Decision-Making in Uncertain Environments." Galore International Journal of Applied Sciences and Humanities 6, no. 3 (2024): 179–84. https://doi.org/10.52403/gijash.20220720.

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Artificial intelligence (AI) has changed the way decisions are made in industries; however, its impact may be limited in situations with uncertainty where cause and effect relationships play a crucial role. While conventional AI models are good at spotting connections between variables they may struggle when it comes to predicting results in fields such as healthcare finance and self-driving vehicles where understanding causation is key. Causal inference an emerging area in AI goes beyond finding correlations and enables systems to make stronger and dependable decisions, in intricate settings.

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Hocquette, Céline, and Andrew Cropper. "Relational Program Synthesis with Numerical Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 6425–33. http://dx.doi.org/10.1609/aaai.v37i5.25790.

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Learning programs with numerical values is fundamental to many AI applications, including bio-informatics and drug design. However, current program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values from multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reasoning. Our approach, which we call NumSynth, uses satisfiability modulo theories solvers to efficiently learn programs with numerical values

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Bredemeier, Brenda Jo Light. "Children's Moral Reasoning and Their Assertive, Aggressive, and Submissive Tendencies in Sport and Daily Life." Journal of Sport and Exercise Psychology 16, no. 1 (1994): 1–14. http://dx.doi.org/10.1123/jsep.16.1.1.

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The purpose of this study was to investigate the relationship between children's moral reasoning and their assertive, aggressive, and submissive action tendencies in sport and daily life contexts. The 106 fourth- through seventh-grade children were asked to reason about hypothetical sport and daily life moral dilemmas and to respond to two behaviorally validated, self-report instruments designed to assess action tendencies in sport and daily life conflict situations. Multiple regression analyses indicated that moral reasoning scores were predictive of action tendencies, with reasoning positive

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Palengka, Inelsi, Dwi Juniati, and Abadi Abadi. "Mathematical reasoning of prospective mathematics teachers in solving problems based on working memory capacity differences." Eurasia Journal of Mathematics, Science and Technology Education 18, no. 12 (2022): em2193. http://dx.doi.org/10.29333/ejmste/12670.

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This study aims to investigate algorithmic reasoning and creative reasoning of prospective mathematics teachers in solving problems. This research is a qualitative research. The research subjects are prospective mathematics teachers with high working memory capacity (HWMC) and low working memory capacity (LWMC). Based on the results on algorithmic reasoning, it is known that for subjects with HWMC using algorithms to solve the given problems. While subjects with LWMC using sum and multiplication rules to solve the given problems. On creative mathematical reasoning it is known that for subjects

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Gonzaga, Yung Bruno de Mello, André Demambre Bacchi, and Vitor Borin Pardo De Souza. "When math legitimizes knowledge: a step by step approach to Bayes’ rule in diagnostic reasoning." Journal of Evidence-Based Healthcare 6 (November 19, 2024): e5903. http://dx.doi.org/10.17267/2675-021xevidence.2024.e5903.

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INTRODUCTION: Many mistakes in clinical practice arise from confusing the probability of a positive test in those with the disease and the probability of having the disease in those who test positive. This misunderstanding leads to overestimating disease probability, diagnosing diseases in healthy individuals, ordering invasive diagnostic tests, and prescribing unnecessary treatments, resulting in unjustified adverse effect, psychological stress, and increased cost. Probabilistic reasoning is an essential skill to mitigate this confusion, and Bayes theorem is an important tool to accomplish th

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Maher, Gabriel. "LLMPC: Large Language Model Predictive Control." Computers 14, no. 3 (2025): 104. https://doi.org/10.3390/computers14030104.

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Recent advancements in planning prompting techniques for Large Language Models have improved their reasoning, planning, and action abilities. This paper develops a planning framework for Large Language Models using model predictive control that enables them to iteratively solve complex problems with long horizons. We show that in the model predictive control formulation, LLM planners act as approximate cost function optimizers and solve complex problems by breaking them down into smaller iterative steps. With our proposed planning framework, we demonstrate improved performance over few-shot pr

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