Relevant bibliographies by topics / Predictive Reasoning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive Reasoning'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Predictive Reasoning"

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Stauffer, E. Shannon. "HIGH TECH VS PREDICTIVE REASONING." Orthopedics 18, no. 10 (October 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 (May 2020): 5–24. http://dx.doi.org/10.1007/s10649-020-09949-0.

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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 (April 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 (May 4, 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 model. Results: The theory of predictive brains is a general, unified and integrated model of cognitive processing based on recent advances in the neurosciences. The predictive brain is characterised as an adaptive, generative, energy-frugal, context-sensitive action-orientated, probabilistic, predictive engine. It responds only to predictive errors and learns by iterative predictive error management, processing and hierarchical neural coding. Conclusion: The default cognitive mode of predictive processing may account for the failure of de-biasing since it is not thermodynamically frugal and thus, may not be sustainable in routine practice. Exploiting predictive brains by employing language to optimise metacognition may be a way forward
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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. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.
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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 (October 1, 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. Information on HBV and HCV infection status, liver function test, AFT, family history of liver cancer, demographic characteristics, lifestyle variables and relevant biomarkers were collected. The occurrence of HCC was ascertained by the linkage of the nationwide cancer registry till the end of 2007. Bayesian clinical reasoning model was adopted by constructing the basic model taken as the prior model for average-risk subject. We then updated the basic model by sequentially incorporating other risk factors for HCC encrypted in the likelihood ratio to form posterior probability that was used for predicting individual risk of HCC. Results: By dint of Bayesian clinical reasoning model with a step-by-step update of the risk of HCC for the sequentially obtained information, a 57-year-old man was predicted to yield 0.69% of HCC risk with the prior model. After history-taking of having hepatitis B carrier (likelihood ratio [LR]: 3.65), family history (LR: 1.43), and no alcohol drinking (LR: 0.89), the posterior risk for HCC was enhanced up to 3.13%. After further biochemical examination, the updated risk of HCC for a man [the following biomarkers [ALT = 30 IU/L (LR: 0.78), AST = 56 IU/L (LR: 8.99), platelets = (203 × /μL) (unit cube of ten) (LR: 0.55)] was increase to 11.07%. Conclusion: We proposed a individually tailored prediction model for HCC by incorporating routine information with a sequential Bayesian clinical reasoning approach.
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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 (April 8, 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 performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (= 5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.
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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 (March 2008): 153–72. http://dx.doi.org/10.1016/j.chb.2007.01.007.

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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 (March 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 Reasoning, and Processing Speed variables were consistently found to be the best predictors of achievement. Multiple Rs ranged from the .60s to .70s. Results provide evidence for the importance of cognitive constructs for predicting achievement and are potentially useful for understanding program planning and Aptitude x Treatment Interaction research.
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Dissertations / Theses on the topic "Predictive Reasoning"

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Bell, J. "Predictive conditionals, nonmonotonicity and reasoning about the future." Thesis, University of Essex, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235132.

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Ng, Sin Wa Serena. "Towards an understanding of the staged model of predictive reasoning." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/7868.

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This study set out to examine the clinical practice of experienced occupational therapists in mental health vocational rehabilitation service in Hong Kong. A combined qualitative and quantitative methodological approach was used to enhance the methodological rigour of the research. Three sub-studies were carried out including a pre-study survey; a semi-structured interview for 6 experienced therapists and a multiple case studies to verify the model of predictive reasoning generated in this research. The findings of this study confirmed the consecutive staged model of decision making, the cyclical predictive reasoning process and its critical components were important in predictive reasoning process. Furthermore, the research alerted that therapist’s ‘Internal References’ affect the process that might exert good or bad influences in the prediction and intervention approaches. From the twenty cases reported and analysed in the multiple case studies, I verified the generated characteristics of the staged model of predictive reasoning process were being evidenced in the daily practices of other experienced occupational therapists. Hence, Predictive Reasoning in occupational therapist was proven as a fundamental scientific, social as Well as psychological process of ascertaining client best suitable choice in vocational rehabilitation. In this research, it has highlighted that they were practicing a bivalent model of practice – scientific in thinking and humanistic in interacting. It has long been a great problem for the professionals to inform the public on their forms and efficacy of practice through scientific rigour. The research methodology employed in this research was an innovative design that responses to both positivist and interpretivist paradigm, to create a new opportunity for occupational therapist to start to reflect on choosing the best suitable research methodology for reporting the real picture of clinical practices.
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Vallée-Tourangeau, Frédéric. "Adjustment to disconfirming evidence in a covariation judgment task : the role of alternative predictive relationships." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=41208.

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This project investigated the impact of sustained disconfirmation on an acquired belief in a covariation judgment task. Both epistemology and the philosophy of science suggest that data which oppose a hypothesis might not dictate the revision of the hypothesis unless an alternative hypothesis can explain the negative evidence and replace the previous hypothesis. As well, the literature on human categorization and reasoning documents a preference for examples and test instances which confirm rather than disconfirm a prior hypothesis. It was therefore predicted that upon the presentation of negative data for an acquired correlational expectation, subjects would abandon their disconfirmed hypothesis with greater ease if the negative evidence was supplemented with alternative hypotheses. A series of four experiments examined this prediction. Using a within-subjects design, subjects first learned that certain predictor variables signalled the presence of certain outcome variables. In a second phase, the outcomes were systematically presented in the absence of the predictors. Adjustment to the negative evidence was measured on the basis of estimates of correlation and the subjects' tendency to predict the presence of the outcomes on trials where the predictors were present. There were three experimental conditions. In the first, an alternative predictor was present on all trials where the outcomes occurred in the absence of the original predictor. In a second, an alternative outcome was present on all trials where the original outcome was absent. In a third, the negative evidence was not framed in terms of either alternative predictors nor alternative outcomes. While all three conditions produced the same reductions in correlation estimates, the condition without alternatives produced perseverance in outcome predictions in the presence of the predictors. This pattern of adjustment was observed in a simulated medical diagnostic task (Experiment 1), and in a nonmedical s
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Alaya, Mili Nourhene. "Managing the empirical hardness of the ontology reasoning using the predictive modelling." Thesis, Paris 8, 2016. http://www.theses.fr/2016PA080062/document.

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Multiples techniques d'optimisation ont été implémentées afin de surmonter le compromis entre la complexité des algorithmes du raisonnement et l'expressivité du langage de formulation des ontologies. Cependant les compagnes d'évaluation des raisonneurs continuent de confirmer l'aspect imprévisible et aléatoire des performances de ces logiciels à l'égard des ontologies issues du monde réel. Partant de ces observations, l'objectif principal de cette thèse est d'assurer une meilleure compréhension du comportement empirique des raisonneurs en fouillant davantage le contenu des ontologies. Nous avons déployé des techniques d'apprentissage supervisé afin d'anticiper des comportements futurs des raisonneurs. Nos propositions sont établies sous forme d'un système d'assistance aux utilisateurs d'ontologies, appelé "ADSOR". Quatre composantes principales ont été proposées. La première est un profileur d'ontologies. La deuxième est un module d'apprentissage capable d'établir des modèles prédictifs de la robustesse des raisonneurs et de la difficulté empirique des ontologies. La troisième composante est un module d'ordonnancement par apprentissage, pour la sélection du raisonneur le plus robuste étant donnée une ontologie. Nous avons proposé deux approches d'ordonnancement; la première fondée sur la prédiction mono-label et la seconde sur la prédiction multi-label. La dernière composante offre la possibilité d'extraire les parties potentiellement les plus complexes d'une ontologie. L'identification de ces parties est guidée par notre modèle de prédiction du niveau de difficulté d'une ontologie. Chacune de nos approches a été validée grâce à une large palette d'expérimentations
Highly optimized reasoning algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL (DL). Nevertheless, reasoning remains a challenge in practice. In overall, a reasoner could be optimized for some, but not all ontologies. Given these observations, the main purpose of this thesis is to investigate means to cope with the reasoner performances variability phenomena. We opted for the supervised learning as the kernel theory to guide the design of our solution. Our main claim is that the output quality of a reasoner is closely depending on the quality of the ontology. Accordingly, we first introduced a novel collection of features which characterise the design quality of an OWL ontology. Afterwards, we modelled a generic learning framework to help predicting the overall empirical hardness of an ontology; and to anticipate a reasoner robustness under some online usage constraints. Later on, we discussed the issue of reasoner automatic selection for ontology based applications. We introduced a novel reasoner ranking framework. Correctness and efficiency are our main ranking criteria. We proposed two distinct methods: i) ranking based on single label prediction, and ii) a multi-label ranking method. Finally, we suggested to extract the ontology sub-parts that are the most computationally demanding ones. Our method relies on the atomic decomposition and the locality modules extraction techniques and employs our predictive model of the ontology hardness. Excessive experimentations were carried out to prove the worthiness of our approaches. All of our proposals were gathered in a user assistance system called "ADSOR"
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Abbas, Kaja Moinudeen. "Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5302/.

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Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with probability distributions of HIV surveillance data coupled with the census population data to estimate the proportion of HIV incidence among the different demographic subgroups. Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the incidence and prevalence of infectious diseases. The methodology is presented in the context of simulated disease outbreak data for influenza. Probabilistic reasoning analysis enhances the understanding of disease progression in order to identify the critical points of surveillance, control and prevention. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest.
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SORMANI, RAUL. "Criticality assessment of terrorism related events at different time scales TENSOR clusTEriNg terroriSm actiOn pRediction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/125509.

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Law Enforcement Agencies (LEAs) are nowadays taking advantage of a wide range of information and intelligence sources (e.g., human intelligence (HUMINT), open source intelligence (OSINT), image analysis (IMINT)) to anticipate potential terroristic actions. Urban environments are nowadays associated with a wide range of vulnerabilities, which create fertile ground for terrorists planning actions against assets and/or citizens. These vulnerabilities stem from the characteristics of the urban environment (e.g., presence of civilians, availability of many and diverse physical infrastructures, complex social/cultural/governmental interactions, high value targets, etc.) have been repeatedly manifested as part of major terrorist attacks, which took place in some of the world’s most important cities (e.g., New York, London, and Madrid). The mitigation of security concerns in the urban environment is therefore a top priority in the social and political agendas of cities. ICT technologies provide help in this direction, for example through surveillance of urban areas, using the proliferating number of low-cost multi-purpose sensors in conjunction with emerging Big Data processing techniques for analyzing them. The thesis illustrates the TENSOR (clusTEriNg terroriSm actiOn pRediction) framework, a near real-time reasoning framework for early identification and prediction of potential threat situations (e.g. terrorist actions). The main objective of TENSOR is to show how patterns of strategic terroristic behaviors, identified analyzing large longitudinal data sets, can be linked to short term activity patterns identified analyzing feeds by “usual” surveillance technologies and that this fusion allows a better detection of terrorist threats. The framework consists of three different modules with the aim of collecting and processing information of the surrounding environment from a variety of sources including physical sensors (e.g. surveillance cameras) and “virtual” sensors (e.g. police officers, citizens). The proposed TENSOR framework processes information sources at different abstraction levels (e.g. sensor information, police inputs, external semantic crafted data sources) and, thru the proposed layered architecture, simulates the three main expert user roles (i.e. operational, tactical and strategic user roles), as indicated in the intelligence analysis domain literature. The framework transforms all the sensors gathered data into symbolic events of interest following a generic scenario-agnostic semantics for terrorist attacks described in literature as terrorist indicators. Thru different reasoning and fusion techniques, the framework proactively detects threats and depicts the situation in near real-time. The framework results have been tested and validated in the European project FP7 PROACTIVE.
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Castillo, Guevara Ramon Daniel. "The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855.

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Cao, Qiushi. "Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.

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Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiques
In the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets
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Bjurén, Johan. "USING CASE-BASED REASONING FOR PREDICTING ENERGY USAGE." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9436.

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In this study, the inability to in a future meet the electricity demand and the urge to change the consumption behavior considered. In a smart grid context there are several possible ways to do this. Means include ways to increase the consumer’s awareness, add energy storages or build smarter homes which can control the appliances. To be able to implement these, indications on how the future consumption will be could be useful. Therefore we look further into how a framework for short-term consumption predictions can be created using electricity consumption data in relation to external factors. To do this a literature study is made to see what kind of methods that are relevant and which qualities is interesting to look at in order to choose a good prediction method. Case Based Reasoning seemed to be able to be suitable method. This method was examined further and built using relational databases. After this the method was tested and evaluated using datasets and evaluation methods CV, MBE and MAPE, which have previously been used in the domain of consumption prediction. The result was compared to the results of the winning methods in the ASHRAE competition. The CBR method was expected to perform better than what it did, and still not as good as the winning methods from the ASHRAE competition. The result showed that the CBR method can be used as a predictor and has potential to make good energy consumption predictions. and there is room for improvement in future studies.
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Khajotia, Burzin K. "CASE BASED REASONING – TAYLOR SERIES MODEL TO PREDICT CORROSION RATE IN OIL AND GAS WELLS AND PIPELINES." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1173828758.

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Books on the topic "Predictive Reasoning"

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We are all apocalyptic now: On the responsibilities of teaching, preaching, reporting, writing, and speaking out. [S. l.]: R. Jensen, 2013.

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Wolsey, Thomas DeVere. Learning to predict and predicting to learn: Cognitive strategies and instructional routines. Boston: Pearson/Allyn & Bacon, 2009.

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Bridgeman, Brent. Predictions of freshman grade-point average from the revised and recentered SAT I, Reasoning Test. New York: College Entrance Examination Board, 2000.

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Matwijkiw, Bronik. Predictive Reasoning in Legal Theory (Applied Legal Philosophy). Ashgate Pub Ltd, 2003.

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Reason and Prediction. Cambridge University Press, 2009.

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Gallagher, Shaun. Enactivist Interventions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198794325.001.0001.

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Enactivist Interventions explores central issues in the contemporary debates about embodied cognition, addressing interdisciplinary questions about intentionality, representation, affordances, the role of affect, and the problems of perception and cognitive penetration, action and free will, higher-order cognition, and intersubjectivity. It argues for a rethinking of the concept of mind, drawing on pragmatism, phenomenology, and cognitive science. It interprets enactivism as a philosophy of nature that has significant methodological and theoretical implications for the scientific investigation of the mind. Enactivist Interventions argues that, like the basic phenomena of perception and action, sophisticated cognitive phenomena like reflection, imagining, and mathematical reasoning are best explained in terms of an affordance-based skilled coping. It thus argues for a continuity that runs between basic action, affectivity, and a rationality that in every case remains embodied. It also discusses recent predictive models of brain function and outlines an alternative, enactivist interpretation that emphasizes the close coupling of brain, body, and environment rather than a strong boundary that isolates the brain in its internal processes. The extensive relational dynamics that integrates the brain with the extra-neural body opens into an environment that is physical, social, and cultural and that recycles back into the enactive process. Cognitive processes are in the world, situated in affordance spaces defined across evolutionary, developmental, and individual histories, and are constrained by affective processes and normative dimensions of social and cultural practices.
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Learning to Predict and Predicting to Learn: Cognitive Strategies and Instructional Routines. Prentice Hall, 2008.

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Superforecasting: The Art and Science of Prediction. Penguin Random House, 2016.

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Superforecasting: The Art and Science of Prediction. Penguin Random House, 2015.

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Superforecasting: The Art and Science of Prediction. Penguin Random House, 2015.

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Book chapters on the topic "Predictive Reasoning"

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Wotawa, Franz. "Reasoning from First Principles for Self-adaptive and Autonomous Systems." In Predictive Maintenance in Dynamic Systems, 427–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_15.

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Bazin, Alexandre, Miguel Couceiro, Marie-Dominique Devignes, and Amedeo Napoli. "An Approach to Identifying the Most Predictive and Discriminant Features in Supervised Classification Problems." In Graph-Based Representation and Reasoning, 48–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86982-3_4.

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Riesterer, Nicolas, Daniel Brand, and Marco Ragni. "The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning." In Lecture Notes in Computer Science, 415–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00111-7_35.

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Martin, Luke J. W. "Predictive Reasoning and Machine Learning for the Enhancement of Reliability in Railway Systems." In Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification, 178–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33951-1_13.

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Toledo, F., S. Moreno, E. Bonet, and G. Martin. "Using Constraint Technology for Predictive Control of Urban Traffic Based on Qualitative and Temporal Reasoning." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 711–16. London: CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-121.

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Sabo, Isabela Cristina, Marco Billi, Francesca Lagioia, Giovanni Sartor, and Aires José Rover. "Unsupervised Factor Extraction from Pretrial Detention Decisions by Italian and Brazilian Supreme Courts." In Lecture Notes in Computer Science, 69–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22036-4_7.

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AbstractPretrial detention is a debated and controversial measure since it is an exception to the principle of the presumption of innocence. To determine whether and to what extent legal systems make excessive use of pretrial detention, an empirical analysis of judicial practice is needed. The paper presents some preliminary results of experimental research aimed at identifying the relevant factors on the basis of which Italian and Brazilian Supreme Courts impose the measure. To analyze and extract the relevant predictive-features, we rely on unsupervised learning approaches, in particular association and clustering methods. As a result, we found common factors between the two legal systems in terms of crime, location, grounds for appeal, and judge’s reasoning.
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Jarrell, Amanda, Jason M. Harley, Susanne Lajoie, and Laura Naismith. "Examining the Relationship Between Performance Feedback and Emotions in Diagnostic Reasoning: Toward a Predictive Framework for Emotional Support." In Lecture Notes in Computer Science, 650–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19773-9_83.

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Yusoff, Aziyati, Norashidah Md Din, Salman Yussof, Assad Abbas, and Samee U. Khan. "Predictive Analytics for Network Big Data Using Knowledge-Based Reasoning for Smart Retrieval of Data, Information, Knowledge, and Wisdom (DIKW)." In Big Data and Computational Intelligence in Networking, 209–26. Boca Raton, FL : CRC Press, [2018]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155678-13.

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Kisselburgh, Lorraine, and Jonathan Beever. "The Ethics of Privacy in Research and Design: Principles, Practices, and Potential." In Modern Socio-Technical Perspectives on Privacy, 395–426. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82786-1_17.

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AbstractThe contexts of sociotechnical privacy have evolved significantly in 50 years, with correlate shifts in the norms, values, and ethical concerns in research and design. We examine these eras of privacy from an ethics perspective, arguing that as contexts expand from the individual, to internet, interdependence, intelligences, and artificiality, they also reframe the audience or stakeholder roles present and broaden the field of ethical concerns. We discuss these ethical issues and introduce a principlist framework to guide ethical decision-making, articulating a strategy by which principles are reflexively applied in the decision-making process, informed by the rich interface of epistemic and ethical values. Next, we discuss specific challenges to privacy presented by emerging technologies such as biometric identification systems, autonomous vehicles, predictive algorithms, deepfake technologies, and public health surveillance and examine these challenges around five ethical principles: autonomy, justice, non-maleficence, beneficence, and explicability. Finally, we connect the theoretical and applied to the practical to briefly identify law, regulation, and soft law resources—including technical standards, codes of conduct, curricular programs, and statements of principles—that can provide actionable guidance and rules for professional conduct and technological development, codifying the reasoning outcomes of ethics.
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Kang, Yong-Bin, Yuan-Fang Li, and Shonali Krishnaswamy. "Predicting Reasoning Performance Using Ontology Metrics." In The Semantic Web – ISWC 2012, 198–214. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35176-1_13.

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Conference papers on the topic "Predictive Reasoning"

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Walkinshaw, Neil. "Using evidential reasoning to make qualified predictions of software quality." In PROMISE '13: 9th International Conference on Predictive Models in Software Engineering. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2499393.2499402.

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Yue, Jia, Anita Raja, and William Ribarsky. "Predictive Analytics Using a Blackboard-Based Reasoning Agent." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.155.

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Cao, Qiushi, Ahmed Samet, Cecilia Zanni-Merk, François de Beuvron, and Christoph Reich. "Combining Evidential Clustering and Ontology Reasoning for Failure Prediction in Predictive Maintenance." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008969506180625.

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Chaplot, Neelam, Praveen Dhyani, and O. P. Rishi. "Predictive Approach of Case Base Reasoning in Artificial Intelligence." In the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905148.

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Tiger, Mattias, and Fredrik Heintz. "Stream Reasoning Using Temporal Logic and Predictive Probabilistic State Models." In 2016 23rd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2016. http://dx.doi.org/10.1109/time.2016.28.

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Thompson, Jennifer, and Jessica Bradley. "Predictive analysis network tool for human knowledge elicitation and reasoning." In 2007 10th International Conference on Information Fusion. IEEE, 2007. http://dx.doi.org/10.1109/icif.2007.4408129.

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Khokhar, Rashid H., and Mohd Noor Md Sap. "Predictive fuzzy reasoning method for time series stock market data mining." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2005. http://dx.doi.org/10.1117/12.603089.

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Hansen, Robert J., David L. Hall, G. William Nickerson, and Shashi Phoha. "Integrated Predictive Diagnostics: An Expanded View." In ASME 1996 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-gt-034.

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In a previous paper (Hansen et al., 1995), a conceptual framework for developing a true prognostic or predictive diagnostic capability was described. The current paper expands on this framework by describing micro-mechanical and dynamic models, sensors and data fusion, signal processing, approximate reasoning, distributed architecture, and human factors research and development being conducted to provide such a capability for a broad range of applications. These include both autonomous and man-in-the-loop decision making about maintenance actions and local and geographically distributed monitoring and data analysis architectures.
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Saputelli, Luigi A., Alexander Verde, and Zameel Haris. "Deriving Unconventional Reservoir Predictive Models From Historic Data Using Case Base Reasoning." In Unconventional Resources Technology Conference. Tulsa, OK, USA: American Association of Petroleum Geologists, 2015. http://dx.doi.org/10.15530/urtec-2015-2155770.

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Montero-Jimenez, Juan Jose, Rob Vingerhoeds, and Bernard Grabot. "Enhancing predictive maintenance architecture process by using ontology-enabled Case-Based Reasoning." In 2021 IEEE International Symposium on Systems Engineering (ISSE). IEEE, 2021. http://dx.doi.org/10.1109/isse51541.2021.9582535.

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Reports on the topic "Predictive Reasoning"

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Perry, Marcus B., Patrick J. Vincent, and Jeremy D. Jordan. Human Predictive Reasoning for Group Interactions. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada535335.

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