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1
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<br>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<br>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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Boytsov, Andrey. "Context reasoning, context prediction and proactive adaptation in pervasive computing systems." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17626.
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The paradigm of pervasive computing aims to integrate the computing technologies in a graceful and transparent manner, and make computing solutions available anywhere and at any time. Different aspects of pervasive computing, like smart homes, smart offices, social networks, micromarketing applications, PDAs, etc. are becoming a part of everyday life. Context of pervasive computing system is any piece of information that can be of possible interest to the system. Context often includes location, time, activity, surroundings, etc. One of the core features of pervasive computing systems is context awareness – the ability to use context information to the benefit of the system. The thesis proposes a set of context prediction and situation prediction methods on top of enhanced situation awareness mechanisms. Being aware of the future context enables a pervasive computing system to choose the most efficient strategies to achieve its stated objectives and therefore a timely response to the upcoming situation can be provided. This thesis focuses on the challenges of context prediction, but in order to become really efficient and useful, context prediction approaches need to be gracefully integrated with different other aspects of reasoning about the context. This thesis proposes a novel integrated approach for proactively working with context information. In order to become efficient, context prediction should be complemented with proper acting on predicted context, i.e. proactive adaptation. The majority of current approaches to proactive adaptation solves context prediction and proactive adaptation problems in sequence. This thesis identifies the shortcomings of that approach, and proposes an alternative solution based on reinforcement learning techniques. The concept of situation provides useful generalization of context data and allows eliciting the most important information from the context. The thesis proposes, justifies and evaluates improved situation modeling methods that allow covering broader range of real-life situations of interest and efficiently reason about situation relationships. The context model defines the pervasive computing system’s understanding of its internal and external environments, and determines the input for context prediction solutions. This thesis proposes novel methods for formal verification of context and situation models that can help to build more reliable and dependable pervasive computing systems and avoid the inconsistent context awareness, situation awareness and context prediction results. The architecture of pervasive computing system integrates all the aspects of context reasoning and governs the interaction and collaboration between different context processing mechanisms. This thesis proposes, justifies and evaluates the architectural support for context prediction methods. The novel architectural solutions allow encapsulating various practical issues and challenges of pervasive computing systems and handling them on low levels of context processing, therefore, supporting the efforts for efficient context prediction and proactive adaptation.<br>Godkänd; 2011; 20110506 (andboy); LICENTIATSEMINARIUM Ämnesområde: Medieteknik/Media Technology Examinator: Professor Arkady Zaslavsky, Institutionen för system och rymdteknik, Luleå tekniska universitet Diskutant: Professor Christian Becker, University of Mannheim, Germany Tid: Måndag den 13 juni 2011 kl 10.00 Plats: A109, Luleå tekniska universitet
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Laird, Philip G. "Predicting juror decisions, the impact of judicial admonitions and moral reasoning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24320.pdf.
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Vladimir, Kurbalija. "Time series analysis and prediction using case based reasoning technology. Analiza i predviđanja toka vremenskih serija pomoću "case-based reasoning" -tehnologije." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2009. http://dx.doi.org/10.2298/NS20091005KURBALIJA.
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This thesis describes one promising approach where a problem of timeseries analysis and prediction was solved by using Case Based Reasoning(CBR) technology. Foundations and main concepts of this technology aredescribed in detail. Furthermore, a detailed study of different approaches intime series analysis is given. System CuBaGe (Curve Base Generator) - Arobust and general architecture for curve representation and indexing timeseries databases, based on Case based reasoning technology, wasdeveloped. Also, a corresponding similarity measure was modelled for agiven kind of curve representation. The presented architecture may beemployed equally well not only in conventional time series (where allvalues are known), but also in some non-standard time series (sparse,vague, non-equidistant). Dealing with the non-standard time series is thehighest advantage of the presented architecture.<br>U ovoj doktorskoj disertaciji prikazan je interesantan i perspektivan pristuprešavanja problema analize i predviđanja vremenskih serija korišćenjemCase Based Reasoning (CBR) tehnologije. Detaljno su opisane osnove iglavni koncepti ove tehnologije. Takođe, data je komparativna analizarazličitih pristupa u analizi vremenskih serija sa posebnim osvrtom napredviđanje. Kao najveći doprinos ove disertacije, prikazan je sistemCuBaGe (Curve Base Generator) u kome je realizovan originalni načinreprezentacije vremenskih serija zajedno sa, takođe originalnom,odgovarajućom merom sličnosti. Robusnost i generalnost sistemailustrovana je realnom primenom u domenu finansijskog predviđanja, gdeje pokazano da sistem jednako dobro funkcioniše sa standardnim, ali i sanekim nestandardnim vremenskim serijama (neodređenim, retkim ineekvidistantnim).
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Ballout, Ali. "Apprentissage actif pour la découverte d'axiomes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4026.
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Cette thèse aborde le défi de l'évaluation des formules logiques candidates, avec un accent particulier sur les axiomes, en combinant de manière synergique l'apprentissage automatique et le raisonnement symbolique. Cette approche innovante facilite la découverte automatique d'axiomes, principalement dans la phase d'évaluation des axiomes candidats générés. La recherche vise à résoudre le problème de la validation efficace et précise de ces candidats dans le contexte plus large de l'acquisition de connaissances sur le Web sémantique.Reconnaissant l'importance des heuristiques de génération existantes pour les axiomes candidats, cette recherche se concentre sur l'avancement de la phase d'évaluation de ces candidats. Notre approche consiste à utiliser ces candidats basés sur des heuristiques, puis à évaluer leur compatibilité et leur cohérence avec les bases de connaissances existantes. Le processus d'évaluation, qui nécessite généralement beaucoup de calculs, est révolutionné par le développement d'un modèle prédictif qui évalue efficacement l'adéquation de ces axiomes en tant que substitut du raisonnement traditionnel. Ce modèle innovant réduit considérablement les exigences en matière de calcul, en utilisant le raisonnement comme un "oracle" occasionnel pour classer les axiomes complexes lorsque cela est nécessaire.L'apprentissage actif joue un rôle essentiel dans ce cadre. Il permet à l'algorithme d'apprentissage automatique de sélectionner des données spécifiques pour l'apprentissage, améliorant ainsi son efficacité et sa précision avec un minimum de données étiquetées. La thèse démontre cette approche dans le contexte du Web sémantique, où le raisonneur joue le rôle d'"oracle" et où les nouveaux axiomes potentiels représentent des données non étiquetées.Cette recherche contribue de manière significative aux domaines du raisonnement automatique, du traitement du langage naturel et au-delà, en ouvrant de nouvelles possibilités dans des domaines tels que la bio-informatique et la preuve automatique de théorèmes. En mariant efficacement l'apprentissage automatique et le raisonnement symbolique, ces travaux ouvrent la voie à des processus de découverte de connaissances plus sophistiqués et autonomes, annonçant un changement de paradigme dans la manière dont nous abordons et exploitons la vaste étendue de données sur le web sémantique<br>This thesis addresses the challenge of evaluating candidate logical formulas, with a specific focus on axioms, by synergistically combining machine learning with symbolic reasoning. This innovative approach facilitates the automatic discovery of axioms, primarily in the evaluation phase of generated candidate axioms. The research aims to solve the issue of efficiently and accurately validating these candidates in the broader context of knowledge acquisition on the semantic Web.Recognizing the importance of existing generation heuristics for candidate axioms, this research focuses on advancing the evaluation phase of these candidates. Our approach involves utilizing these heuristic-based candidates and then evaluating their compatibility and consistency with existing knowledge bases. The evaluation process, which is typically computationally intensive, is revolutionized by developing a predictive model that effectively assesses the suitability of these axioms as a surrogate for traditional reasoning. This innovative model significantly reduces computational demands, employing reasoning as an occasional "oracle" to classify complex axioms where necessary.Active learning plays a pivotal role in this framework. It allows the machine learning algorithm to select specific data for learning, thereby improving its efficiency and accuracy with minimal labeled data. The thesis demonstrates this approach in the context of the semantic Web, where the reasoner acts as the "oracle," and the potential new axioms represent unlabeled data.This research contributes significantly to the fields of automated reasoning, natural language processing, and beyond, opening up new possibilities in areas like bioinformatics and automated theorem proving. By effectively marrying machine learning with symbolic reasoning, this work paves the way for more sophisticated and autonomous knowledge discovery processes, heralding a paradigm shift in how we approach and leverage the vast expanse of data on the semantic Web
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McNiel, Patrick Dean. "The utility of CRT-a sub-scales for understanding and predicting aggressive behaviors." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52297.
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The purpose of this study is to re-analyze existing findings in order to demonstrate and summarize relationships between criteria and the sub-scales/dimensions of the Conditional Reasoning Test for Aggression (CRT-A): Externalizing, Internalizing, and Powerlessness. A CRT-A sub-scale was expected to relate more strongly with criteria categorized as being more relevant to the dimension that is represented by that sub-scale. For criteria that were categorized as relevant to only a subset of the dimensions represented by CRT-A sub-scales, the regression of a criterion on all three sub-scales was expected to create a better fitting model than the regression of a criterion on the CRT-A total score alone. Scales were also expected to interact to predict criteria. This was expected to be most likely when multiple dimensions of implicit aggression were activated by environmental factors to influence specific behaviors. Support was found for all expectations
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Daw, Elbait Gihan Elsir Ahmed. "From cancer gene expression to protein interaction: Interaction prediction, network reasoning and applications in pancreatic cancer." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-19908.
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Microarray technologies enable scientists to identify co-expressed genes at large scale. However, the gene expression analysis does not show functional relationships between co-expressed genes. There is a demand for effective approaches to analyse gene expression data to enable biological discoveries that can lead to identification of markers or therapeutic targets of many diseases.
In cancer research, a number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue such as Pancreatic Ductal Adenocarcinoma (PDAC). PDAC carries very poor prognosis, it eludes early detection and is characterised by its aggressiveness and resistance to currently available therapies. To identify molecular markers and suitable targets, there exist a research effort that maps differentially expressed genes to protein interactions to gain an understanding at systems level. Such interaction networks have a complex interconnected structure, whose the understanding of which is not a trivial task.
Several formal approaches use simulation to support the investigation of such networks. These approaches suffer from the missing knowledge concerning biological systems. Reasoning in the other hand has the advantage of dealing with incomplete and partial information of the network knowledge.
The initial approach adopted was to provide an algorithm that utilises a network-centric approach to pancreatic cancer, by re-constructing networks from known interactions and predicting novel protein interactions from structural templates. This method was applied to a data set of co-expressed PDAC genes. To this end, structural domains for the gene products are identified by using threading which is a 3D structure prediction technique. Next, the Protein Structure Interaction Database (SCOPPI), a database that classifies and annotates domain interactions derived from all known protein structures, is used to find templates of structurally interacting domains. Moreover, a network of related biological pathways for the PDAC data was constructed.
In order to reason over molecular networks that are affected by dysregulation of gene expression, BioRevise was implemented. It is a belief revision system where the inhibition behaviour of reactions is modelled using extended logic programming. The system computes a minimal set of enzymes whose malfunction explains the abnormal expression levels of observed metabolites or enzymes.
As a result of this research, two complementary approaches for the analysis of pancreatic cancer gene expression data are presented. Using the first approach, the pathways found to be largely affected in pancreatic cancer are signal transduction, actin cytoskeleton regulation, cell growth and cell communication. The analysis indicates that the alteration of the calcium pathway plays an important role in pancreas specific tumorigenesis. Furthermore, the structural prediction method reveals ~ 700 potential protein-protein interactions from the PDAC microarray data, among them, 81 novel interactions such as: serine/threonine kinase CDC2L1 interacting with cyclin-dependent kinase inhibitor CDKN3 and the tissue factor pathway inhibitor
2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). These resulting genes were further investigated and some were found to be potential therapeutic markers for PDAC. Since TMPRSS4 is involved in metastasis formation, it is hypothesised that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. The predicted protein-protein network inspired the analysis of the data from two other perspectives. The resulting protein-protein interaction network highlighted the importance of the co-expression of KLK6 and KLK10 as prognostic factors for survival in PDAC
as well as the construction of a PDAC specific apoptosis pathway to study different effects of multiple gene silencing in order to reactivate apoptosis in PDAC.
Using the second approach, the behaviour of biological interaction networks using computational logic formalism was modelled, reasoning over the networks is enabled and the abnormal behaviour of its components is explained. The usability of the BioRevise system is demonstrated through two examples, a metabolic disorder disease and a deficiency in a pancreatic
cancer associated pathway. The system successfully identified the inhibition of the enzyme glucose-6-phosphatase as responsible for the Glycogen storage disease type I, which according to literature is known to be the main reason for this disease. Furthermore, BioRevise was used to model reaction inhibition in the Glycolysis pathway which is known to be affected by Pancreatic cancer.
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Miller, Gina L. "An empirical investigation of a categorization based model of the evaluation formation process as it pertains to set membership prediction." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/29984.
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Loch-Dehbi, Sandra [Verfasser]. "Algebraic, logical and stochastic reasoning for the automatic prediction of 3d building structures / Sandra Loch-Dehbi." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1227990502/34.
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Korrûbel, Jan Laurens. "Predicting recruitment in South African anchovy : analysis of an expert system approach, and the incorporation of probabilistic reasoning." Thesis, University of Cape Town, 1995. http://hdl.handle.net/11427/25869.
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Wiita, Nathan Ellis. "Voluntary turnover prediction comparing the utility of implicit and explicit personality measures /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31786.
Texte intégralRésumé :
Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2010.<br>Committee Chair: Lawrence R. James; Committee Member: Jack Feldman; Committee Member: Richard Catrambone. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Yonge, Katherine Chandler. "Criminal profile accuracy following training in inductive and deductive approaches." Master's thesis, Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-03312008-194642.
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MIRAGLIOTTA, ELISA. "La previsione geometrica: un modello per analizzare un processo cognitivo inerente il problem-solving in geometria." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2020. http://hdl.handle.net/11380/1200566.
Texte intégralRésumé :
La ricerca mira a studiare gli aspetti cognitivi coinvolti nella produzione di previsioni geometriche durante la risoluzione di problemi nell’abito della Geometria Euclidea.
Si può considerare il processo di previsione geometrica come una specifica abilità visuo-spaziale coinvolta nel pensiero geometrico. Infatti, durante il processo di risoluzione di un problema geometrico, un solutore può immaginare diverse trasformazioni della figura e i loro effetti; tali trasformazioni possono essere più o meno coerenti con i vincoli teorici dati dal problema. Inoltre, i prodotti di tali trasformazioni possono inibire o supportare il processo risolutivo.
Ricerche precedenti hanno evidenziato e posto l’attenzione sulla natura degli oggetti geometrici, considerando sia la componente concettuale che la componente figurale. Interpretando il pensiero geometrico in termini di dialettica tra questi due aspetti (Fischbein, 1993), lo studio mira a comprendere il processo di previsione geometrica, inteso come un processo attraverso il quale una figura viene manipolata, i suoi cambiamenti immaginati, mentre alcune proprietà vengono mantenute invarianti.
Il processo di previsione geometrica viene descritto attraverso un modello di generazione di previsioni elaborato ciclicamente: osservando, analizzando secondo un approccio microgenetico e analizzando nuovamente il comportamento di diversi solutori durante la risoluzione di problemi aperti di previsione proposti sia in ambiente carta e penna che in un Ambiente di Geometria Dinamica (AGD).
I problemi aperti di previsione progettati per lo studio sono stati proposti durante interviste task-based a solutori coinvolti su base volontaria. Hanno preso parte allo studio un totale di 37 solutori italiani tra studenti di scuola secondaria di secondo grado, studenti di laurea triennale e magistrale e di dottorato in Matematica. I dati constano di registrazioni video e audio, trascrizioni delle interviste, disegni dei solutori.
La versione finale del modello descrive i processi di previsione di un solutore coinvolto nella risoluzione dei problemi aperti di previsione proposti nello studio. Inoltre, il modello fornisce una lente teorica utile per analizzare le produzioni dei solutori e comprendere più profondamente i processi di previsione. In particolare, il modello chiarisce il ruolo cruciale sia degli elementi teorici introdotti dal solutore durante il processo risolutivo, sia del controllo teorico che i solutori esercitano.
Lo studio ha implicazioni didattiche, utili in particolar modo per la scuola secondaria di secondo grado, per la progettazione di attività volte a promuovere il pensiero geometrico degli studenti e il loro controllo teorico sulle figure geometriche.<br>The purpose of the research is to study cognitive aspects of how geometric predictions are produced during problem-solving activities in Euclidean geometry. The process of geometric prediction is seen as a specific visuo-spatial ability involved in geometrical reasoning. Indeed, when solvers engage in solving a geometrical problem, they can imagine the consequences of transformations of the figure; such transformations can be more or less coherent with the theoretical constraints given by the problem, and the products of such transformations can hinder or promote the problem-solving process.
Previous research has stressed the dual nature of geometrical objects, intertwining a conceptual component and a figural component. Interpreting geometrical reasoning in terms of a dialectic between these two aspects (Fischbein, 1993), this study aims at gaining insight into the cognitive process of geometric prediction, a process through which a figure is manipulated, and its change is imagined, while certain properties are maintained invariant.
This process is described through a model of prediction-generation elaborated cyclically by observing, analyzing through a microgenetic approach, and re-analyzing solvers’ resolution of prediction open problems in a paper-and-pencil environment and in a Dynamic Geometry Environment (DGE).
The prediction open problems designed were proposed during task-based interviews to participants selected on a voluntary basis. Participants were a total of 37 Italian high school students and undergraduate, graduate and PhD students in mathematics. Data are composed of video and audio recordings, transcriptions, solvers’ drawings.
The final version of the model provides a description of the prediction processes accomplished by a solver who engages in the resolution of prediction open problems proposed in this study; it provides a lens through which solvers’ productions can be analyzed and it provides insight into prediction processes. In particular, it sheds light onto the key role played by theoretical elements that are introduced by the solvers during the resolution process and the key role played by the solver’s theoretical control.
The study has implications for the design of activities, especially at the high school level, with the educational objective of fostering students’ geometrical reasoning and in particular their theoretical control over the geometrical figures.
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Ziaeetabar, Fatemeh [Verfasser], Florentin [Akademischer Betreuer] Wörgötter, Florentin [Gutachter] Wörgötter, et al. "Spatio-temporal reasoning for semantic scene understanding and its application in recognition and prediction of manipulation actions in image sequences / Fatemeh Ziaeetabar ; Gutachter: Florentin Wörgötter, Ricarda I. Schubotz, Dieter Hogrefe, Marcus Baum, Carsten Damm, Wolfgang May ; Betreuer: Florentin Wörgötter." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2020. http://d-nb.info/1208918494/34.
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Gribouval, Marie. "Conception d’un système prédictif pour évaluer l’impact d’un dispositif numérique pour l’orientation des lycéens." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0319.
Texte intégralRésumé :
Ce projet de thèse contribue au projet national AILES (Accompagnement à l’intégration des Lycéens dans l’Enseignement Supérieur) et s’intéresse plus particulièrement aux dispositifs numériques destinés à l’aide à l’orientation des lycéens. Le terme « dispositif numérique » désigne dans cette thèse l’ensemble des sites web, applications sur mobile et/ou sur ordinateur. Une grande diversité d’informations est présente dans les dispositifs numériques dédiés à l’orientation. Certains d’entre eux proposent des outils d’échanges, tandis que d’autres disposent de témoignages, par exemple. Nous observons que certains dispositifs ont pour objectif d’informer les lycéens, tandis que d’autres sont dédiés à répondre à leurs questions ou à donner des idées de métiers. En considérant cette diversité, il est difficile pour les professionnels de l’orientation d'identifier quel(s) dispositif(s) est (sont) le(s) plus approprié(s) pour un lycéen, en fonction de son profil. Plusieurs limites apparaissent face à ce problème, (1) le manque de représentation uniforme et formelle des dispositifs numériques d’aide à l’orientation, (2) l’absence de données à grande échelle sur l’impact de ce type de dispositifs, et (3) la possibilité limitée de mesurer un impact auprès de lycéens. Partant de ces limites, cette thèse propose une ontologie et une approche pour évaluer et prédire l’impact sur les lycéens des dispositifs numériques d’aide à l’orientation. Pour cela, nous avons travaillé sur trois propositions :1) La description formelle des dispositifs numériques d'aide à l'orientation. Nous considérons ici la description des dispositifs comme une caractérisation de leur support, structure et contenu. Cette caractérisation permet d'avoir une description uniforme et formelle des dispositifs afin de pouvoir les comparer. Nous avons choisi d'utiliser une ontologie pour décrire ces dispositifs. Les ontologies permettent de décrire les dispositifs par des concepts et des relations possibles entre ces concepts, de créer des instanciations pour chaque dispositif et de réaliser une vérification. 2) La mesure de l'impact de ces dispositifs. Nous nous intéressons uniquement à l'impact du dispositif sur le processus d’orientation des lycéens. Plusieurs types d'impacts peuvent être considérés ainsi que plusieurs profils de lycéens. Dans cette thèse, un modèle est proposé pour mesurer l’impact des dispositifs numériques sur le processus d’orientation des lycéens. 3) La prédiction de l'impact de nouveaux dispositifs. Pour certains dispositifs, la mesure de l’impact n’est pas réalisable. De plus, le nombre de dispositifs pour lequel l'impact est connu est faible. Ainsi, cette thèse propose un modèle basé sur le raisonnement à partir de cas afin de prédire l'impact des dispositifs pour lequel il n'est pas mesurable<br>Those research works contribute to the French national project called AILES (“Accompagnement à l'intégration des Lycéens dans l'Enseignement Supérieur”), and focus particularly on digital devices designed to help high school students with their career guidance. In this PhD thesis, the term "digital device" refers to all websites, mobile and/or computer applications. There is a wide variety of information available about digital guidance devices. Some offer discussion tools, while others offer testimonials, for example. We can observe that some are designed to inform students, while others are dedicated to answering their questions or providing career ideas. Given this diversity, it is difficult for guidance professionals to determine which device(s) is most appropriate for a given high school student's profile. There are several limitations to this problem: (1) the lack of a unified, formal representation of digital guidance devices, (2) the lack of large-scale data on the impact of this type of device, and (3) the limited ability to measure the impact on high school students. Based on these limitations, this thesis proposes an ontology and an approach for evaluating and predicting the impact of digital guidance devices on high school students. To achieve this, we worked on three proposals:1) Formal description of digital guidance devices. Here, we consider the description of devices as a characterization of their support, structure, and content. This characterization provides a uniform and formal description of the devices in order to be compared. We have chosen to use an ontology to describe these devices. Ontologies make it possible to describe devices in terms of concepts and possible relationships between these concepts, to create instantiations for each device, and to perform verification. 2) Measuring the impact of these devices. We are only interested in the impact of the device on the guidance process. Several types of impact can be considered as well as several high school student profiles. In this thesis, we propose a model to measure the impact of digital devices on the guidance process of high school students. 3) Predicting the impact of new devices. For some devices, impact measurement is not feasible. Moreover, the number of devices for which the impact is known is small. Thus, this PhD thesis proposes a model based on case-based reasoning to predict the impact of devices for which it is not measurable
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Wendler, Jan. "Automatisches Modellieren von Agenten-Verhalten." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2003. http://dx.doi.org/10.18452/15008.
Texte intégralRésumé :
In Multi-Agenten-Systemen (MAS) kooperieren und konkurrieren Agenten um ihre jeweiligen Ziele zu erreichen. Für optimierte Agenten-Interaktionen sind Kenntnisse über die aktuellen und zukünftigen Handlungen anderer Agenten (Interaktionsparter, IP) hilfreich. Bei der Ermittlung und Nutzung solcher Kenntnisse kommt dem automatischen Erkennen und Verstehen sowie der Vorhersage von Verhalten der IP auf Basis von Beobachtungen besondere Bedeutung zu. Die Dissertation beschäftigt sich mit der automatischen Bestimmung und Vorhersage von Verhalten der IP durch einen Modellierenden Agenten (MA). Der MA generiert fallbasierte, adaptive Verhaltens-Modelle seiner IP und verwendet diese zur Vorhersage ihrer Verhalten. Als Anwendungsszenario wird mit dem virtuellen Fußballspiel des RoboCup ein komplexes und populäres MAS betrachtet. Der Hauptbeitrag dieser Arbeit besteht in der Ausarbeitung, Realisierung und Evaluierung eines Ansatzes zur automatischen Verhaltens-Modellierung für ein komplexes Multi-Agenten-System.<br>In multi-agent-systems agents cooperate and compete to reach their personal goals. For optimized agent interactions it is helpful for an agent to have knowledge about the current and future behavior of other agents. Ideally the recognition and prediction of behavior should be done automatically. This work addresses a way of automatically classifying and an attempt at predicting the behavior of a team of agents, based on external observation only. A set of conditions is used to distinguish behaviors and to partition the resulting behavior space. From observed behavior, team specific behavior models are then generated using Case Based Reasoning. These models, which are derived from a number of virtual soccer games (RoboCup), are used to predict the behavior of a team during a new game. The main contribution of this work is the design, realization and evaluation of an automatic behavior modeling approach for complex multi-agent systems.
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"An experimental study of structured classroom intervention in a predictive reasoning task." 1999. http://library.cuhk.edu.hk/record=b5889491.
Texte intégralRésumé :
by Lam Kam Po Yolanda.<br>Thesis (M.B.A.)--Chinese University of Hong Kong, 1999.<br>Includes bibliographical references (leaves 43-49).<br>ABSTRACT --- p.ii<br>TABLE OF CONTENT --- p.iii<br>LIST OF TABLES --- p.v<br>ACKNOWLEDGEMENT --- p.vi<br>Chapter<br>Chapter I. --- INTRODUCTION --- p.1<br>Organization of the research report --- p.3<br>Chapter II. --- LITERATURE REVIEW --- p.5<br>Studies of reasoning --- p.5<br>Effects of education and intervention --- p.10<br>Summary --- p.12<br>Chapter III. --- STRUCTURED INTERVENTION --- p.13<br>Operationalization of structure --- p.13<br>The lecture --- p.14<br>Part 1 --- p.15<br>Part 2 --- p.15<br>Chapter IV. --- THE REASONING TASK IN THE STUDY --- p.17<br>Chapter V. --- HYPOTHESIS DEVELOPMENT --- p.19<br>Effect of structured intervention on reasoning in Part 1 --- p.19<br>Effect of structured intervention on reasoning in Part 2 --- p.19<br>Chapter VI. --- METHODOLOGY --- p.21<br>Procedure --- p.21<br>Questionaire --- p.22<br>Participants --- p.22<br>Dependent variables --- p.23<br>Coding procedures --- p.23<br>Chapter VII. --- RESULT --- p.25<br>Effect of structured intervention --- p.25<br>Effect of structured intervention on overall reason generation (Part 1) --- p.26<br>Effect of structured intervention on the construction of one-sided arguments (Part 1) --- p.26<br>Effect of structure on overall reason generation after the participants have determined their position (Part 2) --- p.27<br>Effect of structured intervention on the construction of one-sided arguments (Part 2) --- p.27<br>Control variables --- p.28<br>Chapter VIII. --- DISCUSSION --- p.29<br>Analysis --- p.29<br>Limitations --- p.31<br>Directions for future research --- p.32<br>Structured intervention helps in performance improvement in reasoning - other applications --- p.33<br>APPENDIX --- p.35<br>BIBLIOGRAPHY --- p.43
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Maduma, Eunice Sibongile Sylvia. "The predictive validity of the mental alertness, reading comprehension, arithmetic reasoning and conceptual reasoning tests as used by the Wits Business School." Thesis, 2012. http://hdl.handle.net/10539/11566.
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Leaute, Thomas. "Coordinating Agile Systems through the Model-based Execution of Temporal Plans." 2006. http://hdl.handle.net/1721.1/32537.
Texte intégralRésumé :
Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables.Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles.Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to Âcoach the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plantÂs state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan.Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem. To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed.<br>SM thesis
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"Techniques for Supporting Prediction of Security Breaches in Critical Cloud Infrastructures Using Bayesian Network and Markov Decision Process." Master's thesis, 2015. http://hdl.handle.net/2286/R.I.34910.
Texte intégralRésumé :
abstract: Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures.<br>Dissertation/Thesis<br>Masters Thesis Computer Science 2015
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Groves, Julia. "The predictive validity of the Abstract Reasoning Test and the Raven's Advanced Progressive Matrices Test for the academic results of first year engineering students." Thesis, 2015. http://hdl.handle.net/10539/18270.
Texte intégralRésumé :
A research project submitted in partial fulfilment of the requirements for the degree of MA by coursework and Research Report in the field of Industrial Psychology in the Faculty if Humanities, University of Witwatersrand, Johannesburg, 14 February 2015.<br>This research project examined the predictive validity of the Abstract Reasoning Test
and the Raven’s Advanced Progressive Matrices on the academic results of first
year engineering students. Additionally, biographical variables were examined in
order to assess their contribution to the student’s scores on the psychometric tests.
This research is important as the engineering department were looking to combat the
high failure rate amongst first year engineering students. The department was
looking to use the ART and the Raven’s to foresee the subjects in which students
would struggle, enabling them to prepare extra assistance in this regard. The sample
was the 2013 and 2014 first year engineering students at the University of the
Witwatersrand, Johannesburg (N=395). The analysis showed that the ART and
Raven’s do not predict the academic results of engineering students in their first year
of study. The academic results refer to the marks obtained in the first year subjects
of Chemical and Metallurgical Engineering, Physics, Chemistry, Economics and
Mathematics. However, the biographical variables (especially those of home
language and race) play an important role in contributing to the scores achieved on
both psychometric tests.
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Bulmer, Michael. "Reasoning by term rewriting." Thesis, 1995. https://eprints.utas.edu.au/18996/1/whole_BulmerMichael1995_thesis.pdf.
Texte intégralRésumé :
We propose a broad system for reasoning by term rewriting. Our general
aim is to capture mathematical and scientific reasoning in a coherent system.
To this end we introduce several new processes which allow concrete
descriptions of standard notions.
For deductive reasoning we extend traditional methods for finding canonical
rewrite systems to a general method for systems involving both equations
and inequations. We introduce the notion of side conditions for nontheorems
and show how they provide a new kind of meta-reasoning whereby
an automated reasoner can determine why it failed to prove a given statement.
A method for the automatic proof of inductive theorems by an analogue
of mathematical induction is also presented.
A new algorithm is given for inductively generating conjectures (function
equations) from a set of observations (a rewrite database). This is a process
of scientific induction and we prove some fundamental results linking it to
mathematical induction. Comparisons are given with standard inductive
learning systems, such as FOIL, to illustrate the expressive power of our
algorithm.
We obtain probabilistic measures of the strength of a single conjecture using
statistical testing and an information measure. For a collection of conjectures
we are then able to quantify Popper's well-known falsifiability criterion
for the strength of a theory. We also introduce a non-standard modal operator
to extended our deductive reasoning to reasoning with conjectures.
We use belief dynamics as the framework of an implementation of the reasoning
methods. Consistency analysis, using the same canonical-form algorithm
introduced earlier, allows the reasoner to build a belief set from given
knowledge and to form a working theory from the conjectures it makes.
Again a meta-reasoning is introduced, with the reasoner then able to decide
what experiments need to be carried out when it conjectures more than one
consistent theory from given set of observations.
Dialogues with the reasoner, generated by a prototype implementation of
the work in the thesis, are given to illustrate its behaviour and the links
between the internal language it uses and natural language.
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Lai, Kuan-Hung, and 賴冠宏. "Automobile Sales Prediction Based On Case-Based Reasoning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/t4pn5y.
Texte intégralRésumé :
碩士<br>國立勤益科技大學<br>資訊管理系<br>105<br>Recently, due to the signing of the WTO agreement and the ECFA agreement, and the close cooperation among car dealers, the car market in Taiwan has been turned from a closed market that is protected by the government into an open market. Hence, an effective way for car dealers to lower the cost is to make a prediction about car sales. Case-based analysis is a method for making predictions that does not require communication making among various professional fields, so it can raise efficiency of problem-solving. The method adopted in this study is regression analysis. Through the use of the regression analysis, the researcher tried to find out the environmental-economics factors that influenced the sales of the cars. Also, the method for the prediction of car sales that was based on case-based analysis was adapted. The data of these influential factors were optimized, and standardized. Then, they were combined with the case-based analysis to served as an adapted method of making predictions for car sales. The methods not only solved the problem of data with different measuring units, but also effectively solved the problem that the degree of similarity is influenced by the larger number when the numbers are extremely different and hence it could not show the influences of other factors. The result of this study indicated that the adapted method was superior to the traditional case-based analysis one and regression prediction analysis.
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Wijaya, Ade Kurnia, and 王安康. "APPLICATION OF CASE-BASED REASONING APPROACH TO OUTDOOR DAYLIGHT PREDICTION." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/vgu979.
Texte intégralRésumé :
碩士<br>國立臺灣科技大學<br>營建工程系<br>106<br>At early stage of designing a building, a fast and trustable prediction is superbly needed. The designer needs to propose the design of the building to owner within a very limited amount of time when bidding is held. However, simulation is genuinely needed if the designed building is expected to achieve some points in any green building rating system. Handling simulation which has dozens, hundreds building surrounding require huge additional time for modeling and running the model, but implementing and predicting the output of the simulation is really advantageous for any researcher. Case-based Reasoning (CBR) approach really gives this problem a great solution since in the CBR approach there is no any complicated algorithm that needs very long time to learn if there is any update in the dataset and give the solution almost instantly. Instead of learning the experience, CBR approach retrieve the most similar case then adapt to give the solution which only requires a very short period of time. Based on the fact of these reasons, this research makes a CBR approach to predict the outdoor daylight that influence by outdoor condition. There is some software available to evaluate buildings’ lighting during design stage, but these tools tend to require extended calculation times when it comes to making model or daylight analysis. This research is done using outdoor daylight simulation data which collected from the output of Autodesk Ecotect Analysis. It is used to determine the effect of the outdoor condition of a building which will be represented as building skins. Lastly, the performance of the prediction will be evaluated using MAPE with Leave-one-out validation.
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Pollak, Sara. "The moderating effects of direct and indirect experience on the attitude-behavior relation in the reasoned and automatic processing modes." 1995. https://scholarworks.umass.edu/theses/2291.
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Wei, Liu Hsin, and 劉信偉. "The Application of Case-Based Reasoning for the Prediction of Stock Price Pattern." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/96776992635776749252.
Texte intégralRésumé :
碩士<br>長庚大學<br>資訊管理研究所<br>94<br>Case-Based Reasoning is a common and professional computer application program which solves stock market related issues by studying historical cases.
In this thesis, Case-Based Reasoning is used to analyze stock trends and patterns in order to benefit investors by predicting buying or selling points to make the most profit out of the transaction. The application accomplishes this by feeding information from the existing cases and making similar graphs and charts to determine where the buying and selling points are. Results from the study confirmed that Case-Based Reasoning could be used as a helpful tool for decision making.
Case-Based Reasoning uses historical data in combination with the adjusted results by the designer’s experiments as the foundation of the system. The stock market’s line graphs are used to illustrate the system’s basic structure and to predict future possible stock market trends.
With Case-Based Reasoning Application Program’s efficient calculation characteristic, large investment companies or individual investors can utilize the system to determine profitable buying and selling points in the stock market.
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Tsai, Ai-jhen, and 蔡艾真. "Partisan Mobilized Reasoning and the Prediction of Closet Partisans'' Party Identification." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/78179600321446463074.
Texte intégralRésumé :
碩士<br>國立中山大學<br>政治學研究所<br>103<br>Researchers of partisan voters have been assuming that there is a solid difference between “independent”voters and partisan voters (including leaners). This is hardly a case in the Taiwan context, a democracy of two-party presidential system, where over 40 percent of voters are partisans but claiming independent in most of telephone surveys. Pollsters, researchers, and journalists have been calculating the distribution of party supporters by either omitting these “independent” voters due to the unavailability of the data, or simply apply counterintuitive formula to guess the distribution of the respondents with missing data. This study takes avoid the definition of not-so-well-defined “independent” voters but aiming at these“invisible” or “closet” voters and at finding out their partisan orientation behind their ambivalent answers to telephone surveys.
To do so I took a series of steps, including qualitative and quantitative ones. First I used a representative sample, conducted in January 2014 (N=1,072) in Taiwan via a RDD telephone survey. This survey includes the conventional party identification question plus a serious of theory-based alternative questions that I evaluated to be triggering respondents’ mobilized reasoning about the two major political parties, Kuomingtang (KMT) or Democratic Progressive Party (DPP). I then created an index for partisan respondents of the two political camps, and applied the score patterns to the closet respondents. In another follow up survey (March 2014) that targeted at the closet respondents I found that the correctness of prediction using the index is about 70%. I then targeted and interviewed the most ambivalent closet voters and explored how their partisan mobilized reasoning was (and failed to be) triggered by the alternative survey questions. I concluded with a few survey questions future electoral studies can use for probing closet voters. The rich implications of the findings for improving the accuracy of predicting partisan votes, the debates about the characteristics of independent voters, and the development of partisan mobilized.
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Knox, Grahame Munro. "Clinical prediction rules in physiotherapy clinical education." Thesis, 2019. http://hdl.handle.net/1959.13/1408731.
Texte intégralRésumé :
Research Doctorate - Doctor of Philosophy (PhD)<br>Clinical reasoning is an important skill for physiotherapy students to master, though it can be challenging given their limited clinical experience. Tools exist to aid clinical decision-making, and one that is evidence-based is the clinical prediction rule (CPR). CPRs are algorithms that combine patient characteristics and clinical features into numerical indices to predict the probability of a clinical condition or outcome. Physiotherapy clinical educators play a key role in facilitating clinical reasoning skills in students; however it is unknown whether students learn about CPRs in the clinical setting. A series of four linked studies, using a variety of research methodologies, was conducted to determine the awareness and use of CPRs by physiotherapy students and clinical educators, and then to propose key components for an educational package. Physiotherapy clinical educators and final year pre-professional students were separately surveyed to ascertain their awareness and use of CPRs, including the teaching of CPRs on clinical placement, the relationship with clinical decision-making, and relationship with evidence-based practice. Clinical educators were subsequently interviewed for their views on educational strategies on CPRs for clinical educators. Finally an international panel of experts were consulted in a modified Delphi study to finalise the essential content and optimal methods of delivery for an educational package for clinical educators. Clinical educators reported a poor awareness, understanding and use of CPRs, and few taught them to students. Students similarly reported little awareness and minimal use of CPRs. However those students who were more familiar with CPRs found them useful in promoting their clinical decision-making skills. Clinical educators agreed that an educational package on CPRs for educators would be desirable for improving their clinical use of CPRs and teaching of CPRs. Building on the views of the clinical educators, physiotherapy experts in CPRs recommended the content of this educational package should cover why, when and how to use CPRs clinically, and their limitations. Information on the different types of CPRs, with specific examples, was also identified as important. Online delivery was endorsed via self-directed learning and webinars, along with access to electronic versions of actual CPRs. Self-assessment of learning was also supported. In summary, physiotherapy students and clinical educators have a poor understanding and limited or no clinical experience in using CPRs, but this could possibly be addressed by the development of an evidence-based educational package for clinical educators. Improving physiotherapy clinical educators’ knowledge of CPRs may lead to physiotherapy students gaining a greater understanding and ability to use CPRs while on clinical placement.
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Tasi, Shiu-Ni, and 蔡岫霓. "The Study of Using Case-based Reasoning to the Prediction System of Debris Flow." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/35497791637332289144.
Texte intégralRésumé :
碩士<br>逢甲大學<br>土木工程所<br>95<br>There are many factors that cause debris flow, but the credibility of risk assessment is often not high enough, due to of a lack of debris flow cause factor discussion, or only a couple of representative factors are taken. This study uses Case-Based Reasoning (CBR) as the base, and combines the CBR-Works system to develop a New Similarity Measurement method, based on that to establish debris flow CBR system, so we can have relevant assistant information to reduce the loss of life and money in debris flow events.
The evaluation of the system reasoning effectiveness can be divided into “low possibility of having debris flow”, “it’s possible to have debris flow”, and “high possibility of having landslides”. The reasoning results are represented by their similarities. This study uses the 122 landslide cases in the system, and takes the first 10 similar cases to calculate the mean of these 10 sets. The mean distribution situation of those 122 similarity degrees is analyzed, and the critical value of the occurrences of debris flow is defined. Through the system implementation and evaluation, the result shows this CBR system can provide valuable and accurate predictions for debris flow hazard assessments.
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Wang, Yu-Kai, and 王昱凱. "Design and Implementation for Smart Home Systems Based on Grey Prediction and Fuzzy Reasoning." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/cqkq6k.
Texte intégralRésumé :
碩士<br>國立臺北科技大學<br>電機工程系研究所<br>100<br>In recent years, global weather has caused extreme phenomenon. The family often stays in carbon monoxide poisoning, and the high indoor carbon dioxide concentration causes people uncomfortable, tired or headachy, event vomiting. However, in present market condition, the indoor environmental monitoring system does not do a finer classification for the environmental monitoring level without predict function. In addition, most sensors are wired, and difficult for deployment.
Therefore, in order to improve these problems, this paper proposes a grey prediction and fuzzy reasoning application to the degree of environmental risk assessment of the smart home system. In our framework , the smart home system is divided into two parts, the control of smart appliances and indoor environmental monitoring. The system combines wireless sensor networks (WSN) to improve the problems of the sensor erection and wiring difficulties, as well as provides indoor appliance wireless monitoring and sensor data acquisition. The remote user can use smart phones with the global system for mobile communications (GSM) to control the remote home appliance, and use SMS services to look up a remote electrical state simple. While the computer monitor end collects the data of carbon monoxide, carbon dioxide, temperature sensor data to analyze, making use of grey projected to collect temperature data for the next period forecast, comparing with the forecast temperature and the current temperature to get the temperature change. Finally, by using the fuzzy reasoning of temperature change, carbon monoxide, carbon dioxide data to do calculations to infer the indoor environmental risk rating, the proposed system provides corresponding measures to have a more secure family living environment.
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Huang, Cheng-tao, and 黃政道. "Applying Data Mining Approach and Case-Based Reasoning to Develop a Carotid Diagnostic Prediction System." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/46564527470981013878.
Texte intégralRésumé :
碩士<br>國立臺灣科技大學<br>工業管理系<br>101<br>With the medical technology and people knowledge have been promoted, people are aware that heath examination is important. However, living environment and dietary habit gradually change. Lead to number of patients with chronic illnesses is rising. In which, cerebrovascular disease is the top ten leading causes of death and one of the main reason for the increase in the number of people with disabilities.
This study got a brain health examination database from cooperation of medical center. Hope that through data mining techniques and heuristic algorithm apply in prediction of carotid diagnostic. Applications include feature selection and prediction model construction, predictive accuracy of model for training is 81.1% and for testing is 82.1%.
However, the simple prediction result is not enough for what assist doctors. Therefore, this study constructs case-based reasoning rules to assist doctors what obtain information more. Doctors can through health examination report to analyze and predict carotid diagnostic result for patients.
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Chen, Po-yu, and 陳柏宇. "An intelligent system for predicting stock trading strategies using case-based reasoning and neural network." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/s864g4.
Texte intégralRésumé :
碩士<br>國立中山大學<br>資訊工程學系研究所<br>97<br>The rapid growth of the Internet has shaped up the global economy. The stock market information is thus more and more transparent. Although the investors can get more helpful information to judge future trend of the stock market, they may get wrong judgments because the stock market data are too huge to be completely analyzed. Therefore, the purpose of this study is to develop an artificial stock market analyst by employing the information technology with high speed and performance, as well as integrating the artificial intelligence techniques. We exploit case-based reasoning to simulate the analysts in using history stock market data, employ the artificial neural network to imitate the analysts in analyzing the macrofactors of stock market, and apply the fuzzy logic to humanize the artificial stock market analyst in making judgments close to the real stock market analysts. The artificial stock market analyst would use the modified case-based reasoning system combined with the artificial neural network, and incorporate the designed membership functions for macrofactors of stock market. We expect the system to improve the accuracy of Taiwan electric stock price prediction by applying macrofactors from the technical analysis indicators and financial crisis factors, and make better stock trading strategies.
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Lin, Shin-Chieh, and 林士傑. "The High Speed Auto-focusing for Industry Inspection Based on Fuzzy Reasoning and Grey Prediction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/3mq59y.
Texte intégralRésumé :
碩士<br>國立臺北科技大學<br>自動化科技研究所<br>95<br>This thesis proposes a high speed auto-focusing strategy which dramatic increasing the speed and improving the reliability of the auto-focusing technique. This strategy integrates the fuzzy reasoning and grey prediction algorithm. Firstly, the local and global slopes of sharpness function, calculated by the specified image caught by CCD, are feeding into fuzzy reasoning scheme as input variables. The corresponding moving step is calculated from fuzzy reasoning scheme. Then, the gray prediction model is adapted to predict the peak of the sharpness function curve after the local or global slopes decreasing. Therefore, the focusing mechanism comes back to the previous position which is the focusing position. The strategy can reduce focusing time around the focusing position. Finally, an experimental setup, implemented on a PC with Microsoft windows and RTX subsystem, is installed to verify the performance of proposed strategy. Comparing the experimental results of proposed strategy with traditional binary-search algorithm, the results reveal that this strategy can reduce the focusing time.
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Lee, Yun-Chen, and 李昀宸. "Power-Saving Methods Using Grey Prediction and Fuzzy Reasoning to Transmission Power Control for Mobile Sensor Networks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/9d3mg7.
Texte intégralRésumé :
碩士<br>國立臺北科技大學<br>電機工程系研究所<br>101<br>In mobile sensor networks, in addition to the energy consumed by the mobile, there 3 kinds of energy consumption of sensor nodes are: transmission, reception and idle. Among them, the maximum energy consumption is data transfer. Therefore, the purpose of the transmission power control (TPC) is to reduce the overhead data transmission, and increase the life of the sensor nodes. Existing sensor network for mobile transmission power control must be offline to create the path prediction model for real-time adjustment of the transmission power, and this approach leads to additional overhead costs and energy consumption.
This thesis proposes an on-line predictive approach to immediately adjust the transmission power and maintain good transmission performance of TPC. Input the values received by the base station (BS) -transmission power, the signal strength value and the prediction value of the next signal strength- into the fuzzy logic system. This produces a new transmission power value input into the end device (ED) to dynamically adjust the transmission power command. This paper has two stages of setting: 1) Initial stage, BS broadcasts through different size levels, and gives proper transmission power settings that reduce the transmission of the initial mobile ED energy. 2) Dynamic adjustment stage, since ED has mobility, this thesis makes use of the advantages of the gray prediction and fuzzy inference to produce a new transmission power such that the transmission energy and packet loss can be reduced. Grey is able to use a small amount of data to forecast in dynamic real-time. The result suits for different mobile sensor network environments. Therefore, the proposed method not only can dynamically adjust transmit power to reduce ED transmission energy consumption, but also can improve network performance by the estimating scheme. Our approach furthermore can prolong the life of the entire network.
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Chiu, Hsiang-Ju, and 邱相茹. "Model establishment of predicting recurrent status of liver cancer patients using multiple measurements case-based reasoning method." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/67529558374992928282.
Texte intégralRésumé :
碩士<br>國立臺灣大學<br>生醫電子與資訊學研究所<br>101<br>Due to the progress of medicine, clinical data are increased very rapidly and biochemistry laboratory items are multiply measured with the subsequent consultations of patients. These multiple measurements clinical data may become another problem during analysis. This study proposes a practicable method to appropriately handle the clinical data with multiple measurements. Based on the case-based reasoning (CBR) method, we propose a multiple measurements CBR (MMCBR) method, extended from single measurement CBR (SingleCBR), for analyzing clinical data. The research target of this study is the prediction of recurrent status of liver cancer patients after receiving the first treatment in one year. We randomly separated dataset into four subsets, and the average results of classification using three-fold cross validation in four random datasets are analyzed, respectively. The results show models with better performance in the mean accuracy of four random datasets. Combination CBR could produce comparable results with SingleCBR and might have better stability than that of SingleCBR according to the standard deviation of accuracy. The mean sensitivities of MMCBR and Combination CBR in most combinations are better than those of SingleCBR. In this study, five feature selection approaches, different time periods of clinical data merging, and different weights are examined for establishing a predictive model.
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Chen, Yu-Chao, and 陳堉照. "Application of Unsupervised Fuzzy Neural Network Reasoning Model for the prediction of the strength of High-Performance Concrete." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/70430347689492363565.
Texte intégralRésumé :
碩士<br>國立交通大學<br>土木工程系<br>88<br>In addition to the four basic ingredients of the conventional concrete, i.e., Portland cement, fine and coarse aggregates, and water, the making of HPC needs to incorporate the supplementary cementations materials, such as fly ash and blast furnace slag, and chemical admixtures such as superplasticizer. Hence, the characteristics of HPC are much more complex and hard to build an effective model to estimate the strength by mathematical model. Proposed by Hung and Jan, Unsupervised Fuzzy Neural Network(UFN) Reasoning Model has been proved an effective learning model in engineering design. In this work, a UFN reasoning model has been apply to predict the strength properties of high-performance concrete (HPC) mixes. About thousand data collected from different labs are used as training instances. For the sake of comparison, a supervised neural network with BFGS learning model is also employed to train the training data. The simulation results reveal that the UFN reasoning model can not only reason hundreds training data in reasonable computational time but also yield superior prediction of HPC strength to those generated through supervised neural network learning models.
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Ziaeetabar, Fatemeh. "Spatio-temporal reasoning for semantic scene understanding and its application in recognition and prediction of manipulation actions in image sequences." Thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0005-1381-3.
Texte intégral
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"An Adaptive Approach to Securing Ubiquitous Smart Devices in IoT Environment with Probabilistic User Behavior Prediction." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.40829.
Texte intégralRésumé :
abstract: Cyber systems, including IoT (Internet of Things), are increasingly being used ubiquitously to vastly improve the efficiency and reduce the cost of critical application areas, such as finance, transportation, defense, and healthcare. Over the past two decades, computing efficiency and hardware cost have dramatically been improved. These improvements have made cyber systems omnipotent, and control many aspects of human lives. Emerging trends in successful cyber system breaches have shown increasing sophistication in attacks and that attackers are no longer limited by resources, including human and computing power. Most existing cyber defense systems for IoT systems have two major issues: (1) they do not incorporate human user behavior(s) and preferences in their approaches, and (2) they do not continuously learn from dynamic environment and effectively adapt to thwart sophisticated cyber-attacks. Consequently, the security solutions generated may not be usable or implementable by the user(s) thereby drastically reducing the effectiveness of these security solutions.
In order to address these major issues, a comprehensive approach to securing ubiquitous smart devices in IoT environment by incorporating probabilistic human user behavioral inputs is presented. The approach will include techniques to (1) protect the controller device(s) [smart phone or tablet] by continuously learning and authenticating the legitimate user based on the touch screen finger gestures in the background, without requiring users’ to provide their finger gesture inputs intentionally for training purposes, and (2) efficiently configure IoT devices through controller device(s), in conformance with the probabilistic human user behavior(s) and preferences, to effectively adapt IoT devices to the changing environment. The effectiveness of the approach will be demonstrated with experiments that are based on collected user behavioral data and simulations.<br>Dissertation/Thesis<br>Doctoral Dissertation Computer Science 2016
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Daw, Elbait Gihan Elsir Ahmed [Verfasser]. "From cancer gene expression to protein interaction: interaction prediction, network reasoning and applications in pancreatic cancer / by, eingereicht von Gihan Elsir Ahmed Daw Elbait." 2009. http://d-nb.info/1007282223/34.
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"Data Driven Inference in Populations of Agents." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53476.
Texte intégralRésumé :
abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive. More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.<br>Dissertation/Thesis<br>Doctoral Dissertation Computer Science 2019
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Zolezzi, Stefano Alberto. "The effectiveness of dynamic assessment as an alternative aptitude testing strategy." Thesis, 1995. http://hdl.handle.net/10500/17878.
Texte intégralRésumé :
The present study sets out to evaluate the effectiveness of a dynamic approach to aptitude
testing. It was proposed that it is not always appropriate to use conventional aptitude
tests to predict future academic success in the South African context. The study posited
the belief that an alternative testing format could be facilitated by using a test-train-test
procedure within a learning potential paradigm.
The learning potential paradigm as formulated through Vygotskian and Feuersteinian
theory is operationalised in the form of a Newtest Battery. The Newtest procedure is in
direct contrast to traditional approaches to aptitude testing. The latter approaches both
implicitly and explicitly adopt a static view of ability, whereas the Newtest approach
focuses on the learning potential of the testee, as well as consequent performance.
However, the assessment of learning potential poses problems of its own. Modifications
were introduced to ensure that the Newtest format is both appropriate and
psychometrically defensible. The construction and evaluation of the Newtest Battery is
described.
A sample of both advantaged and disadvantaged students were tested on a battery of
traditional aptitude tests. This group of students was contrasted with another sample of
both advantaged and disadvantaged students who undertook the Newtest Battery in the
modified dynamic testing format. The traditional measures of aptitude were found to be
invalid predictors of university success. Matric results showed a relationship with
academic success for both groups. The Newtest measures enhanced the prediction of
academic success for both advantaged and disadvantaged students. The Deductive
Reasoning dynamic measure was found to be a valid predictor of university success for the
disadvantaged students.
The results thus successfully extend the learning potential paradigm into the realm of
group aptitude testing. The validity of traditional aptitude test measures has been brought
into question by the findings of the study. The study points the way forward to a more
equitable and relevant aptitude testing procedure.
Finally, it was shown that the testing environment forms part of the socio-educational
context. Personnel involved in the administration of aptitude tests are given guidelines
\vi th the aim of equalising the test process.<br>Psychology of Education<br>D. Ed. (Psychology of Education)