Relevant bibliographies by topics / Adaptive intelligence model

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Academic literature on the topic 'Adaptive intelligence model'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Adaptive intelligence model"

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Sternberg, Robert J. "A Theory of Adaptive Intelligence and Its Relation to General Intelligence." Journal of Intelligence 7, no. 4 (2019): 23. http://dx.doi.org/10.3390/jintelligence7040023.

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Intelligence typically is defined as consisting of “adaptation to the environment” or in related terms. Yet, it is not clear that “general intelligence” or g, traditionally conceptualized in terms of a general factor in a psychometrically-based hierarchical model of intelligence, provides an optimal way of defining intelligence as adaptation to the environment. Such a definition of adaptive intelligence would need to be biologically based in terms of evolutionary theory, would need to take into account the cultural context of adaptation, and would need to take into account whether thought and behavior labeled as “adaptively intelligent” actually contributed to the perpetuation of the human and other species, or whether it was indifferent or actually destructive to this perpetuation. In this article, I consider the similarities and differences between “general intelligence” and “adaptive intelligence,” as well as the implications especially of the differences.
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Mohammadalian, Sareh, Eslam Nazemi, and Mohammad Jafar Tarokh. "Propose a Conceptual Model of Adaptive Competitive Intelligence (ACI)." International Journal of Business Intelligence Research 4, no. 4 (2013): 22–32. http://dx.doi.org/10.4018/ijbir.2013100102.

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In recent years, competitive changes and pressure on business environment have increased importance of competitive fields. Competitive intelligence is one of the commercial tools available in this field. It extracts helpful information from competitive environment and, after accurate analysis, generates effective strategies for the organization. High speed of changes, uncertainty, complexity and so on are among the characteristics of competitive environments. Consequently, approach of competitive intelligence must be adaptable to any kind of changes occurring in the competitive environment. In this paper, a conceptual model was presented for adaptive competitive intelligence. The proposed model which was a conceptual model was evaluated along the paper and the results were discussed. Increasing sustainability of competitive power was one of the most important outcomes of the recommended model.
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Goel, Ashok K., and Eleni Stroulia. "Functional device models and model-Based diagnosis in adaptive design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 4 (1996): 355–70. http://dx.doi.org/10.1017/s0890060400001670.

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AbstractWe analyze the diagnosis task in the context of adaptive design and redesign of physical devices. We identify three types of diagnosis tasks that differ in the types of information they take as input: the design does not achieve a desired function of the device, the design results in an undesirable behavior, and a specific structural element in the design misbehaves. We describe a model-based approach for solving the diagnosis task in the context of adaptive design and redesign. This approach uses functional models that explicitly represent the device functions and use them to organize teleological and causal knowledge about the device. In particular, we describe a specific kind of functional model called structure—behavior—function (SBF) models in which the causal behaviors of the device are specified in terms of flow of substances through components. We illustrate the use of SBF models with three examples from Kritik2, a knowledge system that designs new devices by retrieving, diagnosing, and adapting old device designs.
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Li, Wei. "Neuron Network Model-Based Control System Model." Applied Mechanics and Materials 339 (July 2013): 143–46. http://dx.doi.org/10.4028/www.scientific.net/amm.339.143.

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When the control object complicate conventional PID control accuracy will be significantly reduced. In recent years, with the gradual improvement of the people of artificial intelligence theory, analog neural networks has been rapid development, the emergence of a large number of excellent algorithm and the means of achieving, from single neuron PID algorithm and with gain control neuron system PID algorithm, two aspects discusses the process of adaptive neuron PID algorithm to achieve accuracy improved adaptive neuron system controller PID algorithm based on this analysis.
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MERTOGUNO, J. S., and N. G. BOURBAKIS. "KYDON VISION SYSTEM: THE ADAPTIVE LEARNING MODEL." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 453–69. http://dx.doi.org/10.1142/s021821309500022x.

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In this paper, an adaptive learning model for an autonomous vision system multi-layers architecture, called Kydon, are presented, modeled, and analyzed. In particular two critical (deletion and saturation) points on the learning curve are evaluated. These points represent two extreme states on the learning process. The Kydon architecture consists of ‘k’ layers array processors. The lowest layers consists of lower-level processing layers, and the rest consists of higher-level processing layers. The interconnectivity of the PEs in each array is based on a full hexagonal mesh structure. Kydon uses graph models to represent and process the knowledge, extracted from the image. The knowledge base of Kydon is distributed among its PE’s. A unique model for evolving knowledge base has been developed especially for Kydon in order to provide it with some intelligence properties.
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Bendor, Jonathan, and Terry M. Moe. "An Adaptive Model of Bureaucratic Politics." American Political Science Review 79, no. 3 (1985): 755–74. http://dx.doi.org/10.2307/1956842.

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In this article we outline a new framework for the formal analysis of bureaucratic politics. It departs from standard neoclassical approaches, notably those of Niskanen (1971) and Peltzman (1976), in several important respects. First our approach explicitly models a system of three-way interaction among bureaus, politicians, and interest groups. Second, it allows for institutional features of each type of participant. Third, it is a model of dynamic process. Fourth, participants make choices adoptively rather than optimizing. Fifth, participants are only minimally informed.The result is a dynamic model of adaptive behavior, very much in the spirit of Simon's (1947) behavioral tradition, that offers a new perspective on political control, bureaucratic power, and the “intelligence of democracy.”
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Hanagud, S., B. J. Glass, and A. J. Calise. "Artificial intelligence-based model-adaptive approach to flexible structure control." Journal of Guidance, Control, and Dynamics 13, no. 3 (1990): 534–44. http://dx.doi.org/10.2514/3.25367.

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Kim, Joo-Chang, and Kyungyong Chung. "Neural-network based adaptive context prediction model for ambient intelligence." Journal of Ambient Intelligence and Humanized Computing 11, no. 4 (2018): 1451–58. http://dx.doi.org/10.1007/s12652-018-0972-3.

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Pradhan, Rabindra Kumar, Lalatendu Kesari Jena, and Sanjay Kumar Singh. "Examining the role of emotional intelligence between organizational learning and adaptive performance in Indian manufacturing industries." Journal of Workplace Learning 29, no. 3 (2017): 235–47. http://dx.doi.org/10.1108/jwl-05-2016-0046.

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Purpose The purpose of this study is to examine the relationship between organisational learning and adaptive performance. Furthermore, the study investigates the moderating role of emotional intelligence in the perspective of organisational learning for addressing adaptive performance of executives employed in manufacturing organisations. Design/methodology/approach The participants were selected through purposive sampling. The study has used established scales on organisational learning, emotional intelligence and adaptive performance to collect data from the respondents. Data were analysed through structural equation modelling using linear structural model (LISREL 8.72). Moderated regression analysis was carried out through a series of hierarchical models to test the hypotheses. The authors have followed the interaction graphs recommended by Aiken and West (1991) to check the moderating effect of emotional intelligence. Findings The result of the study indicates a significant relationship between organisational learning and adaptive performance. The significant moderation effect was observed in the interaction graph, wherein it was found that the relationship between organisational learning and adaptive performance was stronger among the executives with high levels of emotional intelligence and weaker for those having low levels of emotional intelligence. Originality/value The present study gains significance through highlighting the role of emotional intelligence in the perspective of organisational learning and, thus, offers insights to practitioners for addressing adaptive performance of employees.
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Hammell, Robert J., and Thomas Sudkamp. "An adaptive hierarchical fuzzy model." Expert Systems with Applications 11, no. 2 (1996): 125–36. http://dx.doi.org/10.1016/0957-4174(96)00040-1.

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Dissertations / Theses on the topic "Adaptive intelligence model"

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Neves, Pedro. "An Implementation Framework for Emotion Based Adaptive Agents." Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/14023.

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The work presented in this document is part of the project AutoFocus: Adaptive Self-Improving Multi-Agent Systems' that is being developed at the research unit LabMAg, which objective is the implementation of multi-agent systems based on autonomous entities capable of self-optimized and adaptive behaviors. The notion of autonomic computation, like other notions that also imply pro-active computation, is based on autonomous entities that actively work to achieve their objectives and have the ability to dynamically adjust to changes in their environment, constrained by time and resource limits. In the approach used by the AutoFocus project, that adaptation to change and the regulation of the agent's capabilities, result from the combination of cognitive aspects with emotional based aspects. The agent model defined and used by the AutoFocus project is the Agent Flow Model. The task that corresponded to the work presented in this document was to develop a platform for the Agent Flow Model. It was intended, with this platform, to provide a tool that enables the rapid deployment and monitoring of agents based on this model. The developed work consisted in the analysis and design, oriented to objects, implementation and testing of components of this platform
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Turner, Roy Marvin. "A schema-based model of adaptive problem solving." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/9156.

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Tang, Meng. "The Adaptive Intelligent Model for Process Diagnosis, Prediction and Control." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for produksjons- og kvalitetsteknikk, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-328.

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This research work focuses at first on the intelligent model development for process state (special for fault) detection, behavior prediction and process control for complex industrial processes. In the model architecture, Fuzzy Neural Networks (FNNs) are employed as process state classifiers for process state (fault) detection; other (different) Neural Networks (NNs) models are applied for system identification of process characteristics in different process states. The model detects process states (faults) and predicts process behavior according to process input and historical behaviors, whose combination of influences generates the final results of process state (fault) detection and quantitative prediction. The whole model is constructed based on Fuzzy TS NARX models. Secondly, an optimal model is designed to two purposes, one is for optimal process diagnosis and another is for optimal prediction. To time varying processes, an adaptive strategy and algorithm, applying the Least Squares algorithm, has been developed for model adaptability to cover time depending process changes. Thirdly, a specific state space equation of discrete time varying system is being derived from the model. In the state space equation, the state transition matrix A is determined by the fuzzy degree of process state classification produced by process historical behavior in time t instant, and the input transition matrix B by process real input in time t instant. The state observer vector H is determined by optimization results generated by model adaptive or optimal scheme. Finally, to confirm the validity of the theoretical results from above, an application case has been studied for supply forecasting. The study and application results indicate that the model not only has good performance for fault detection, but also provides excellent quantitative prediction of process output. It can be applied in process state (fault) detection, diagnosis and prediction for process behavior, as well as fault predictive control.
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Wang, Olivier. "Adaptive Rules Model : Statistical Learning for Rule-Based Systems." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX037/document.

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Les Règles Métiers (Business Rules en anglais, ou BRs) sont un outil communément utilisé dans l’industrie pour automatiser des prises de décisions répétitives. Le problème de l’adaptation de bases de règles existantes à un environnement en constante évolution est celui qui motive cette thèse. Des techniques existantes d’Apprentissage Automatique Supervisé peuvent être utilisées lorsque cette adaptation se fait en toute connaissance de la décision correcte à prendre en toute circonstance. En revanche, il n’existe actuellement aucun algorithme, qu’il soit théorique ou pratique, qui puisse résoudre ce problème lorsque l’information connue est de nature statistique, comme c’est le cas pour une banque qui souhaite contrôler la proportion de demandes de prêt que son service de décision automatique fait passer à des experts humains. Nous étudions spécifiquement le problème d’apprentissage qui a pour objectif d’ajuster les BRs de façon à ce que les décisions prises aient une valeur moyenne donnée.Pour ce faire, nous considérons les bases de Règles Métiers en tant que programmes. Après avoir formalisé quelques définitions et notations dans le Chapitre 2, le langage de programmation BR ainsi défini est étudié dans le Chapitre 4, qui prouve qu’il n’existe pas d’algorithme pour apprendre des Règles Métiers avec un objectif statistique dans le cas général. Nous limitons ensuite le champ d’étude à deux cas communs où les BRs sont limités d’une certaine façon : le cas Borné en Itérations dans lequel, quelles que soit les données d’entrée, le nombre de règles exécutées en prenant la décision est inférieur à une borne donnée ; et le cas Linéaire Borné en Itérations dans lequel les règles sont de plus écrite sous forme Linéaire. Dans ces deux cas, nous produisons par la suite un algorithme d’apprentissage basé sur la Programmation Mathématique qui peut résoudre ce problème. Nous étendons brièvement cette formalisation et cet algorithme à d’autres problèmes d’apprentissage à objectif statistique dans le Chapitre 5, avant de présenter les résultats expérimentaux de cette thèse dans le Chapitre 6<br>Business Rules (BRs) are a commonly used tool in industry for the automation of repetitive decisions. The emerging problem of adapting existing sets of BRs to an ever-changing environment is the motivation for this thesis. Existing Supervised Machine Learning techniques can be used when the adaptation is done knowing in detail which is the correct decision for each circumstance. However, there is currently no algorithm, theoretical or practical, which can solve this problem when the known information is statistical in nature, as is the case for a bank wishing to control the proportion of loan requests its automated decision service forwards to human experts. We study the specific learning problem where the aim is to adjust the BRs so that the decisions are close to a given average value.To do so, we consider sets of Business Rules as programs. After formalizing some definitions and notations in Chapter 2, the BR programming language defined this way is studied in Chapter 3, which proves that there exists no algorithm to learn Business Rules with a statistical goal in the general case. We then restrain the scope to two common cases where BRs are limited in some way: the Iteration Bounded case in which no matter the input, the number of rules executed when taking the decision is less than a given bound; and the Linear Iteration Bounded case in which rules are also all written in Linear form. In those two cases, we later produce a learning algorithm based on Mathematical Programming which can solve this problem. We briefly extend this theory and algorithm to other statistical goal learning problems in Chapter 5, before presenting the experimental results of this thesis in Chapter 6. The last includes a proof of concept to automate the main part of the learning algorithm which does not consist in solving a Mathematical Programming problem, as well as some experimental evidence of the computational complexity of the algorithm
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García, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
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Yan, Jingsheng. "Platoon modal operations under vehicle autonomous adaptive cruise control model." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07102009-040612/.

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Beyan, Timur. "A New Fuzzy-chaotic Modelling Proposal For Medical Diagnostic Processes." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12605924/index.pdf.

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Main reason of this study is to set forth the internal paradox of the basic approach of the artificial intelligence in the medical field to by discussing on the theoretical and application levels and to suggest solutions in theory and practice against that. In order to rule out the internal paradox in the medical decision support systematic, a new medical model is suggested and based on this, concepts such as disease, health, etiology, diagnosis and treatment are questioned. Meanwhile, with the current scientific data, a simple application sample based on how a decision making system which was set up by fuzzy logic and which is based on the perception of human as a complex adaptive system has been explained. Finally, results of the research about accuracy and validity of this application, current improvements based on the current model and the location on the artificial intelligence theory is discussed.
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Moreno, Cavidad Julian. "Reference model for adaptive and intelligent educational systems supported by learning objects." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/70222.

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A aprendizagem assistida por computador, conhecida mais amplamente com o nome genérico de e-learning, converteu-se numa poderosa ferramenta com amplas potencialidades dentro do campo educativo. Mesmo assim, uma das maiores críticas que esta recebe é que na maioria dos casos os cursos que são implementados seguem um enfoque “one size fits all”, isto é, que todos os alunos recebem exatamente o mesmo conteúdo e da mesma maneira desconhecendo suas necessidades particulares. Esta falha radica não só na falta de interação direita entre aluno e tutor, senão também na falta de um desenho instrucional apropriado que considere alguns dos diversos enfoques disponíveis hoje em dia. Existem diversos enfoques que procuram solucionar este problema e adaptar o processo de ensino os estudantes. Pode-se dizer que na vanguarda de estes enfoques encontram-se os Sistemas Educacionais Inteligentes Adaptativos, os quais combinam as funcionalidades de dois enfoques: os Sistemas Hipermídia Educacionais Adaptativos y os Sistemas Tutoriais Inteligentes. Embora, logo de uma extensa revisão bibliográfica, se encontrou que existe ainda um inconveniente importante com este tipo de sistemas e em particular com seus modelos de referência: ou são demasiado simples, incluindo somente umas poucas funcionalidades; ou são demasiado complexos, o que dificulta seu desenho e implementação. Considerando este panorama, o objetivo principal de esta tese foi a definição de um modelo de referência intentando alcançar esse equilíbrio esquivo, de tal maneira que permita o desenho de cursos que se adaptem de una maneira efetiva e inteligente ao progresso e características de cada estudante, mas sem ser demasiado complexo. Outra propriedade importante desse modelo és que integra o uso de Objetos de Aprendizagem, promovendo assim a flexibilidade e a usabilidade. Para alcançar este objetivo geral, três sub modelos foram considerados: um modelo do domínio, um modelo do estudante y um modelo do tutor. O primeiro serve para estruturar o domínio de conhecimento e foi definido usando a noção de objetivo de aprendizagem junto com um esquema flexível multi-nível com operações opcionais de pré-requisitos. O segundo visa caracterizar aos estudantes e considera informação pessoal, de conhecimento e psico-cognitiva. O terceiro pode ser considerado como o coração do sistema e define as funcionalidades adaptativas consideradas: sequenciamento y navegação, apresentação de conteúdo, evacuação, y suporte colaborativo. Com o fim de clarificar os três sub modelos, assim como todos seus componentes e relações, se presentou um exemplo de instanciação que se denominou Doctus, o qual consiste em una ferramenta de autor para cursos adaptativos. Doctus não somente serviu para exemplificar o uso do modelo de referência em sua totalidade, mas também para refinar os sub modelos e alguns procedimentos involucrados. Como parte final de esta tese, se realizou também a implementação e validação preliminar de Doctus. Isto foi feito com 51 sujeitos, professores em diversos níveis de formação. Os resultados obtidos em esta etapa foram sobressalientes no sentido que todas as funcionalidades adaptativas foram bem avaliadas e todos os pesquisados manifestaram seu entusiasmo por contar com uma ferramenta que lhes ajudara em seus práticas docentes considerando a seus estudantes como indivíduos particulares.<br>Computer Aided Learning, known more widely with the generic name of e-learning, has become a powerful tool with lots of potentialities within educational field. Even though, one of the main critics that it receives is that in most cases the implemented courses follows a “one size fits all” approach, which means that all students receive the same content in the same way being unaware of their particular needs. This problem is not due only to the absence of direct interaction between student and tutor, but also because of the lack of an appropriate instructional design. There are several approaches which deal with this issue and look for adapt the teaching process to students. One could say that in the top of those approaches the Adaptive and Intelligent Educational Systems are situated, which merges the functionalities of two approaches: the Adaptive Educational Hypermedia Systems and the Intelligent Tutoring Systems. Nevertheless, after an extensive literature review, a major inconvenience is still found for this kind of systems and particularly for their reference models: or they are too simple, including just a few functionalities; or they are too complex, which difficult their design and implementation. Considering this panorama, the main objective of this dissertation thesis was the definition of a reference model trying to reach such an elusive equilibrium, in such a way that allows the design of courses which adapt themselves in an intelligent and effective way to the progress and characteristics of each student but without being too complex. Another important feature is that this model integrates Learning Objects, promoting this way flexibility and reusability. In order to achieve this general objective, three sub-models were considered: a domain model, a student model and a tutor model. The first one serves to structure the knowledge domain and was defined using the notion of learning goal and a flexible multilevel schema with optional prerequisite operations. The second one aids to characterize students and considered personal, knowledge and psycho-cognitive information. The third one may be considered as the hearth of the system and defines the adopted adaptive functionalities: sequencing and navigation, content presentation, assessment, and collaborative support. With the aim of clarify the three sub-models, as well as all their components and relationships, an instantiation example was also presented. Such an instantiation was called Doctus, an authoring tool for adaptive courses. Doctus was not only helpful to exemplify the setup of the referece model as a whole, but also to refine sub-models and several procedures envolved. As final part of the dissertation, the implementation and preliminary validation of Doctus was performed. This was done with 51 subjects, teachers from different formation levels. The obtained results in this stage were outstanding, all the adaptive functionalities were well evaluated and all of those polled felt enthusiastic about counting with a tool for helping them in their teaching practices considering students as particular individuals.<br>El aprendizaje asistido por computador, conocido más ampliamente con el nombre genérico de e-learning, se ha convertido en una poderosa herramienta con amplias potencialidades dentro del campo educativo. Aun así, una de las mayores críticas que este recibe es que en la mayoría de los casos los cursos que son implementados siguen un enfoque “one size fits all”, es decir, que todos los alumnos reciben exactamente el mismo contenido y de la misma manera desconociendo sus necesidades particulares. Esta falla radica no sólo en la falta de interacción directa entre alumno y tutor, sino también en la falta de un diseño instruccional apropiado que considere diversos de los enfoques disponibles hoy en día. Existen diversos enfoques que buscan solucionar este problema y adaptar el proceso de enseñanza a los estudiantes. Se podría decir que a la vanguardia de estos enfoques se encuentran los Sistemas Educacionales Inteligentes Adaptativos, los cuales combinan las funcionalidades de dos enfoques: los Sistemas Hipermedia Educacionales Adaptativos y los Sistemas Tutoriales Inteligentes. Sin embargo, luego de una extensa revisión bibliográfica, se encontró que existe aún un inconveniente importante con este tipo de sistemas y en particular con sus modelos de referencia: o son demasiado simples, incluyendo solamente unas pocas funcionalidades; o son demasiado complejos, lo cual dificulta su diseño e implementación. Considerando este panorama, el objetivo principal de esta tesis fue la definición de un modelo de referencia intentando alcanzar tal equilibrio esquivo, de tal manera que permita el diseño de cursos que se adapten de una manera efectiva e inteligente al progreso y características de cada estudiante pero sin ser demasiado complejo. Otra propiedad importante de dicho modelo es que integra el uso de Objetos de Aprendizaje, promoviendo así la flexibilidad y la reusabilidad. Con el fin de alcanzar este objetivo general, tres sub modelos fueron considerados: un modelo del dominio, un modelo del estudiante y un modelo del tutor. El primero sirve para estructurar el dominio de conocimiento y fue definido empleando la noción de objetivo de aprendizaje junto con un esquema flexible multinivel con operaciones opcionales de prerrequisitos. El segundo busca caracterizar los estudiantes y considera información personal, de conocimiento y psico-cognitiva. El tercero puede ser considerado como el corazón del sistema y define las funcionalidades adaptativas consideradas: secuenciamiento y navegación, presentación de contenido, evaluación, y soporte colaborativo. Con el fin de clarificar los tres sub modelos, así como todos sus componentes y relaciones, se presentó además un ejemplo de instanciación. Tal instanciación se denominó Doctus, el cual consiste en una herramienta de autor para cursos adaptativos. Doctus no solamente sirvió para ejemplificar el uso del modelo de referencia en su totalidad, sino también para refinar los sub modelos y algunos procedimientos involucrados. Como parte final de esta tesis, se realizó también la implementación y validación preliminar de Doctus. Esto se hizo con 51 sujetos, todos profesores en diversos niveles de formación. Los resultados obtenidos en esta etapa fueron sobresalientes en el sentido que todas las funcionalidades adaptativas fueron bien evaluadas y todos los encuestados manifestaron su entusiasmo por contar con una herramienta que les ayudara en sus prácticas docentes considerando a sus estudiantes como individuos particulares.
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Brug, Arnold van de. "A framework for model-based adaptive training." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/1177.

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Wang, Yu. "Adaptive control and learning using multiple models." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10783473.

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<p> Adaptation can have different objectives. Compared to a learning behavior, which is mainly to optimize the rewards/experience obtained through the learning process, adaptive control is a type of adaptation that follows a specific target guided by a controller. Although the targets may be different, the two types of adaption share common research interests.</p><p> One of the popular research techniques for studying adaptation is the use of multiple models, where the system will utilize information from multiple environment observers instead of one to improve the adaptation behavior in terms of stability, speed and accuracy. In this thesis, applications of multiple models for two types of adaptation, adaptive control and learning, will be investigated separately. For adaptive control, the research focuses on second-level adaptation, which is a new multiple-model-based approach; for learning, the multiple model concept is designed and embedded into a type of reinforcement scheme: learning automata.</p><p> The stability, robustness and performance of second-level adaptation will be first investigated in the context of various environments, including time-varying plants and noisy disturbances. Then, a new design of second-level adaptation for general systems and input-output accessible systems will be discussed. The reasons for the improved performance using second-level adaptation are analyzed theoretically. The second part of the thesis contributes to a new method of learning automata using multiple models. The method is first applied to a two-state (binary) reward environment in the simplest case, and it is later extended to the feed-forward case when multiple states or actions are presented. Finally, general reinforcement learning automata for network cases will be discussed. In all cases, simulation studies are given, wherever appropriate, to demonstrate the improvement in performance compared to conventional approaches.</p><p>
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Books on the topic "Adaptive intelligence model"

1

Angelov, Plamen P. Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Physica-Verlag HD, 2002.

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Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, 1992.

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Adaptive bidding in single sided auctions under uncertainty: An agent-based approach in market engineering. Birkhauser Verlag AG, 2007.

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Japan) Workshop on the Future of VR and AR Interfaces (2001 Yokohama. The future of VR and AR interfaces: Multi modal, humanoid, adaptive, and intelligent : proceedings of the workshop at IEEE Virtual Reality 2001, Yokohama, Japan, March 14, 2001. GMD-Forschungszentrum Informationstechnik, 2001.

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Brabazon, Anthony. Natural Computing in Computational Finance. Springer-Verlag Berlin Heidelberg, 2010.

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IEEE Conference on Decision and Control (34th 1995 New Orleans, La.). Proceedings of the 34th IEEE Conference on Decision and Control: December 13-15, 1995, New Orleans Hilton Riverside, New Orleans, Louisiana, USA. Institute of Electrical and Electronics Engineers, 1995.

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Booker, Lashon. Perspectives on Adaptation in Natural and Artificial Systems (Proceedings Volume in the Santa Fe Institute Studies in the Sciences of Complexity.). Oxford Univ Pr (Sd), 2007.

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Lashon, Booker, ed. Perspectives on adaptation in natural and artificial systems. Oxford University Press, 2005.

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(Editor), Lashon Booker, Stephanie Forrest (Editor), Melanie Mitchell (Editor), and Rick Riolo (Editor), eds. Perspectives on Adaptation in Natural and Artificial Systems (Proceedings Volume in the Santa Fe Institute Studies in the Sciences of Complexity.). Oxford University Press, USA, 2005.

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Booker, Lashon, Stephanie Forrest, Melanie Mitchell, and Rick Riolo, eds. Perspectives on Adaptation in Natural and Artificial Systems. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162929.001.0001.

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This book is a collection of essays exploring adaptive systems from many perspectives, ranging from computational applications to models of adaptation in living and social systems. The essays on computation discuss history, theory, applications, and possible threats of adaptive and evolving computations systems. The modeling chapters cover topics such as evolution in microbial populations, the evolution of cooperation, and how ideas about evolution relate to economics. The title Perspectives on Adaptation in Natural and Artificial Systems honors John Holland, whose 1975 Book, Adaptation in Natural and Artificial Systems has become a classic text for many disciplines in which adaptation play a central role. The essays brought together here were originally written to honor John Holland, and span most of the different areas touched by his wide-ranging and influential research career. The authors include some of the most prominent scientists in the fields of artificial intelligence evolutionary computation, and complex adaptive systems. Taken together, these essays present a broad modern picture of current research on adaptation as it relates to computers, living systems, society, and their complex interactions.
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Book chapters on the topic "Adaptive intelligence model"

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Liu, Tao, Li Zhang, and Binbin Shi. "Adaptive Immune Response Network Model." In Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04020-7_96.

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Sui, Xiao-hong, and Yan-ying Wang. "A Algorithm Based on Adaptive Smoothing and Mixed Probability Model." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23887-1_67.

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Ke, Peng, Yuanxiang Li, and Xin Nie. "Self-adaptive Optimization for Traffic Flow Model Based on Evolvable Hardware." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33478-8_32.

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Sousa, Ricardo, and João Gama. "Online Multi-label Classification with Adaptive Model Rules." In Advances in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44636-3_6.

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Yu, Chao, Fenghui Ren, and Minjie Zhang. "An Adaptive Bilateral Negotiation Model Based on Bayesian Learning." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30737-9_5.

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Stiborek, Jan, Martin Grill, Martin Rehak, Karel Bartos, and Jan Jusko. "Game Theoretical Model for Adaptive Intrusion Detection System." In Transactions on Computational Collective Intelligence XV. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45910-2_7.

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Stiborek, Jan, Martin Grill, Martin Rehak, Karel Bartos, and Jan Jusko. "Game Theoretical Model for Adaptive Intrusion Detection System." In Transactions on Computational Collective Intelligence XV. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44750-5_7.

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Diaz, Elva, Eunice Ponce-de-Leon, Pedro Larrañaga, and Concha Bielza. "Probabilistic Graphical Markov Model Learning: An Adaptive Strategy." In MICAI 2009: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05258-3_20.

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van Dijk, Marit, and Jan Treur. "Physical Activity Contagion and Homophily in an Adaptive Social Network Model." In Computational Collective Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98443-8_9.

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Boucheny, Christian, Richard Carrillo, Eduardo Ros, and Olivier J. M. D. Coenen. "Real-Time Spiking Neural Network: An Adaptive Cerebellar Model." In Computational Intelligence and Bioinspired Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_18.

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Conference papers on the topic "Adaptive intelligence model"

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Penev, Kalin. "Free Search - A Model of Adaptive Intelligence." In 2009 International Conference on Adaptive and Intelligent Systems (ICAIS). IEEE, 2009. http://dx.doi.org/10.1109/icais.2009.24.

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Yu, Xiaohui, Yang Liu, and Aijun An. "An Adaptive Model for Probabilistic Sentiment Analysis." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.284.

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Hiura, Takuya, and Shin Morishita. "Application of Swarm Intelligence to a Vibration Monitoring System." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3734.

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The technology of swarm intelligence has been applied to a mechanical vibration monitoring system composed of a network of units equipped with sensors and actuators. The expression of “swarm intelligence” was first used in 1988 in the context of cellular robotic systems, where lots of simple agents may generate self-organized patterns through mutual interactions. There are various examples of the swarm intelligence in the natural environment, a swarm of ants, birds or fish. In this sense, the network of agents in a swarm may have some kind of intelligence or higher function than those appeared in a simple agent, which is defined as the swarm intelligence. The concept of swarm intelligence may be applied in diverse engineering fields such as flexible pattern recognition, adaptive control system, or intelligent monitoring system, because some kind of intelligence may emerge on the network without any special control system. In this study, a simulation model of a five degree-of-freedom lumped mass-spring system was prepared as an example of a mechanical dynamic system. Five units composed of a displacement sensor and a variable damper as actuator were assumed to be placed on each mass of the system. Each unit was connected to each other to exchange the information of state variables measured by sensors on each unit. Because the network of units configured as a mutual connected neural network, a kind of artificial intelligence, the network of units may memorize the several expected vibration-controlled patterns and may produce the signal to the actuators on the unit to reduce the vibration of target system. The simulation results showed that the excited vibration was reduced autonomously by selecting the position where the damping should be applied.
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Gao, Jun, Weiming Hu, Xiaoqin Zhang, and Xi Li. "Adaptive Distributed Intrusion Detection Using Parametric Model." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.113.

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Algarni, Abdulmohsen, Yuefeng Li, and Yue Xu. "Adaptive Information Filtering Based on PTM Model (APTM)." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.305.

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Liu, Yanbin, and Yuping Lu. "Nonlinear Fuzzy Robust Adaptive Control of a Longitudinal Hypersonic Aircraft Model." In 2009 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 2009. http://dx.doi.org/10.1109/aici.2009.13.

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He Qiang. "The design of model reference adaptive control." In International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1410.

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Tu, Pham A., and Kenko Uchida. "Model Reference Adaptive Control for Constrained Linear Systems." In Artificial Intelligence and Applications / Modelling, Identification, and Control. ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-084.

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Chen, Jiahong, Jiuqiang Han, and Xinman Zhang. "An Adaptive Single Model of Maneuvering Target Tracking." In 2006 International Conference on Computational Intelligence and Security. IEEE, 2006. http://dx.doi.org/10.1109/iccias.2006.294227.

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Smyrnakis, Michalis, Hongyang Qu, Dario Bauso, and Sandor Veres. "Multi-model Adaptive Learning for Robots Under Uncertainty." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008927700500061.

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Reports on the topic "Adaptive intelligence model"

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Wegman, Edward J. Adaptive Multi-Modal Data Mining and Fusion for Autonomous Intelligence Discovery. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada495346.

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