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1
Jordaan, Edzard Adolf Biermann. "Intelligent elevator control based on adaptive learning and optimisation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95999.
Full textAbstract:
Thesis (MEng)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Machine learning techniques have been around for a few decades now and
are being established as a pre-dominant feature in most control applications.
Elevators create a unique control application where traffic flow is controlled
and directed according to certain control philosophies. Machine learning techniques
can be implemented to predict and control every possible traffic flow
scenario and deliver the best possible solution. Various techniques will be implemented
in the elevator application in an attempt to establish a degree of
artificial intelligence in the decision making process and to be able to have
increased interaction with the passengers at all times.
The primary objective for this thesis is to investigate the potential of machine
learning solutions and the relevancy of such technologies in elevator control
applications. The aim is to establish how the research field of machine learning,
specifically neural network science, can be successfully utilised with the
goal of creating an artificial intelligent (AI) controller. The AI controller is
to adapt to its existing state and change its control parameters as required
without the intervention of the user.
The secondary objective for this thesis is to develop an elevator model that represents
every aspect of the real-world application. The purpose of the model
is to improve the accuracy of existing theoretical and simulated models, by
modulating previously unknown and complex variables and constraints. The
aim is to create a complete and fully functional testing platform for developing
new elevator control philosophies and testing new elevator control mechanisms. To achieve these objectives, the main focus is directed to how waiting time,
probability theory and power consumption predictions can be optimally utilised
by means of machine learning solutions. The theoretical background is provided
for these concepts and how each subject can potentially influence the
decision making process. The reason why this approach has been difficult to
implement in the past, is possibly mainly due to the lack of adequate representation
for these concepts in an online environment without the continuous
feedback from an Expert System. As a result of this thesis, the respective
online models for each of these concepts were successfully developed in order
to deal with the identified shortcomings.
The developed online models for projected waiting times, probability networks
and power consumption feedback were then combined to form a new Intelligent
Elevator Controller (IEC) structure as opposed to the Expert System
approach, mostly used in present computer based elevator controllers.<br>AFRIKAANSE OPSOMMING: Masjienleertegnieke bestaan al vir 'n paar dekades en is 'n oorwegende kenmerk
in hedendaagse beheertoestelle. Hysbakke skep 'n unieke beheertoepassing,
waar verkeersvloei beheer en gerig kan word volgens sekere beheer loso e.
Masjienleertegnieke kan geïmplementeer word om elke moontlike verkeersvloei
situasie te voorspel en te beheer en die beste moontlike oplossing te lewer.
Verskeie tegnieke sal in die tesis ondersoek word in 'n poging om 'n mate van
kunsmatige intelligensie in die besluitneming proses te skep asook verhoogte
interaksie met die passasiers te alle tye.
Die prim^ere doel van hierdie tesis is om die potensiaal van 'n masjienleer oplossing
en die toepaslikheid van dit in hysbakbeheertoepassings te ondersoek.
Die doel is om vas te stel hoe die navorsing in die veld van die masjienleer,
spesi ek in neurale netwerk wetenskappe, suksesvol aangewend kan word met
die doel om 'n kunsmatige intelligente beheerder te skep. Die kunsmatige intelligente
beheerder moet kan aanpas by sy onmidelike omgewing en sy beheer
parameters moet kan verander soos nodig sonder die ingryping van die gebruiker.
Die sekond^ere doelwit vir hierdie tesis is om 'n hysbakmodel, wat elke aspek
van die werklike w^ereld verteenwoordig, te ontwikkel. Die doel van die
model is om die akkuraatheid van die bestaande teoretiese en gesimuleerde
modelle te verbeter deur voorheen onbekende en komplekse veranderlikes en
beperkings in ag te neem. Die doel is om 'n funksionele toetsplatform te skep vir die ontwikkeling van
nuwe hysbakbeheer loso e en vir die toets van nuwe hysbakbeheermeganismes.
Om hierdie doelwitte te bereik, is die hoo okus gerig om wagtyd, waarskynlikheidsteorie
en kragverbruik voorspellings optimaal te gebruik deur middel
van die masjienleer oplossings. Die teoretiese agtergrond is voorsien vir hierdie
konsepte en hoe elke konsep potensieel die besluitneming kan beïnvloed. Die
rede waarom hierdie benadering moeilik was om te implementeer tot hede, is
moontlik te wyte aan die gebrek aan voldoende verteenwoordiging vir hierdie
konsepte in 'n aanlynomgewing sonder die voortdurende terugvoer van 'n Deskundige
Stelsel. As gevolg van hierdie tesis word die onderskeie aanlynmodelle
vir elk van hierdie konsepte suksesvol ontwikkel om die geïdenti seerde tekortkominge
te oorkom.
Die ontwikkelde aanlynmodelle vir geprojekteerde wagtye, waarskynlikheidsnetwerke
en kragverbruik terugvoer is dan gekombineer om 'n nuwe intelligente
hysbakbeheerder struktuur te skep, in teenstelling met die Deskundige Stelsel
benadering in die huidige rekenaar gebaseerde hysbakbeheerders.
2
Kseibat, Dawod. "Adaptive intelligent tutoring for teaching modern standard Arabic." Thesis, University of Bedfordshire, 2010. http://hdl.handle.net/10547/134371.
Full textAbstract:
The aim of this PhD thesis is to develop a framework for adaptive intelligent tutoring systems (ITS) in the domain of Modern Standard Arabic language. This framework will comprise of a new approach to using a fuzzy inference mechanism and generic rules in guiding the learning process. In addition, the framework will demonstrate another contribution in which the system can be adapted to be used in the teaching of different languages. A prototype system will be developed to demonstrate these features. This system is targeted at adult English-speaking casual learners with no pre-knowledge of the Arabic language. It will consist of two parts: an ITS for learners to use and a teachers‘ tool for configuring and customising the teaching rules and artificial intelligence components among other configuration operations. The system also provides a diverse teaching-strategies‘ environment based on multiple instructional strategies. This approach is based on general rules that provide means to a reconfigurable prediction. The ITS determines the learner‘s learning characteristics using multiple fuzzy inferences. It has a reconfigurable design that can be altered by the teacher at runtime via a teacher-interface. A framework for an independent domain (i.e. pluggable-domain) for foreign language tutoring systems is introduced in this research. This approach allows the system to adapt to the teaching of a different language with little changes required. Such a feature has the advantages of reducing the time and cost required for building intelligent language tutoring systems. To evaluate the proposed system, two experiments are conducted with two versions of the software: the ITS and a cut down version with no artificial intelligence components. The learners used the ITS had shown an increase in scores between the post-test and the pre-test with learning gain of 35% compared to 25% of the learners from the cut down version.
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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.
Full textAbstract:
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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Weerasinghe, A. "A General Model of Adaptive Tutorial Dialogues for Intelligent Tutoring Systems." Thesis, University of Canterbury. Computer Science and Software Engineering, 2013. http://hdl.handle.net/10092/8732.
Full textAbstract:
Adaptive tutorial dialogues have been successfully employed by ITSs to facilitate deep learning of conceptual domain knowledge. But none of the approaches used for generating dialogues have been used across instructional domains and tasks. The objective of this project was twofold: (i) to propose a general model that provides adaptive dialogue support in both well- and ill-defined instructional tasks (ii) to explore whether adaptive tutorial dialogues are better than non-adaptive dialogues in acquiring domain knowledge.
Our model provides adaptive dialogue support by identifying the concepts that the student has most difficulty with, and then selecting the tutorial dialogues corresponding to those concepts. The dialogues are customised based on the student’s knowledge and explanation skills, in terms of the length and the exact content of the dialogue. The model consists of three parts: an error hierarchy, tutorial dialogues and rules for adapting them.
We incorporated our model into EER-Tutor, a constraint-based tutor that teaches database design. The effectiveness of adaptive dialogues compared to non-adaptive dialogues in learning this ill-defined task was evaluated in an authentic classroom environment. The results revealed that the acquisition of the domain knowledge (represented as constraints) of the experimental group who received adaptive dialogues was significantly higher than their peers in the control group with non-adaptive dialogues. We also incorporated our model into NORMIT, a constraint-based tutor that teaches data normalization. We repeated the experiment using NORMIT in a real-world class room environment with a much smaller group of students (18 in NORMIT study vs 65 in EER-Tutor study) but did not find significant differences. We also investigated whether our model could support dialogues in logical database design and fraction addition using paper-based methods.
Our evaluation studies and investigations on paper indicated that our model can provide adaptive support for both ill-and well-defined tasks associated with a well-defined domain theory. The results also indicated that adaptive dialogues are more effective than non-adaptive dialogues in teaching the ill-defined task of database design.
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Grundtman, Per. "Adaptive Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648.
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The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.
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Almohammadi, Khalid. "Type-2 fuzzy logic based systems for adaptive learning and teaching within intelligent e-learning environments." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/17211/.
Full textAbstract:
The recent years have witnessed an increased interest in e-learning platforms that incorporate adaptive learning and teaching systems that enable the creation of adaptive learning environments to suit individual student needs. The efficiency of these adaptive educational systems relies on the methodology used to accurately gather and examine information pertaining to the characteristics and needs of students and relies on the way that information is processed to form an adaptive learning context. The vast majority of existing adaptive educational systems do not learn from the users’ behaviours to create white-box models to handle the high level of uncertainty and that could be easily read and analysed by the lay user. The data generated from interactions, such as teacher–learner or learner–system interactions within asynchronous environments, provide great opportunities to realise more adaptive and intelligent e-learning platforms rather than propose prescribed pedagogy that depends on the idea of a few designers and experts. Another limitation of current adaptive educational systems is that most of the existing systems ignore gauging the students' engagements levels and mapping them to suitable delivery needs which match the students' knowledge and preferred learning styles. It is necessary to estimate the degree of students’ engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in small and large-scale online learning platforms. Furthermore, most of the current adaptive educational systems are used within asynchronous e-learning contexts as self-paced e-learning products in which learners can study in their own time and at their own speed, totally ignorant of synchronous e-learning settings of teacher-led delivery of the learning material over a communication tool in real time. This thesis presents novel theoretical and practical architectures based on computationally lightweight T2FLSs for lifelong learning and adaptation of learners’ and teachers’ behaviours in small- and large-scale asynchronous and synchronous e-learning platforms. In small-scale asynchronous and synchronous e-learning platforms, the presented architecture augments an engagement estimate system using a noncontact, low-cost, and multiuser support 3D sensor Kinect (v2). This is able to capture reliable features including head pose direction and hybrid features of facial expression to enable convenient and robust estimation of engagement in small-scale online and onsite learning in an unconstrained and natural environment in which users are allowed to act freely and move without restrictions. We will present unique real-world experiments in large and small-scale e-learning platforms carried out by 1,916 users from King Abdul-Aziz and Essex universities in Saudi Arabia and the UK over the course of teaching Excel and PowerPoint in which the type 2 system is learnt and adapted to student and teacher behaviour. The type-2 fuzzy system will be subjected to extended and varied knowledge, engagement, needs, and a high level of uncertainty variation in e-learning environments outperforming the type 1 fuzzy system and non-adaptive version of the system by producing better performance in terms of improved learning, completion rates, and better user engagements.
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Serce, Fatma Cemile. "A Multi-agent Adaptive Learning System For Distance Education." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609220/index.pdf.
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The adaptiveness provides uniquely identifying and monitoring the learner&rsquo<br>s learning activities according to his/her respective profile. The adaptive intelligent learning management systems (AILMS) help a wide range of students to achieve their learning goals effectively by delivering knowledge in an adaptive or individualized style through online learning settings. This study presents a multi-agent system, called MODA, developed to provide adaptiveness in learning management systems (LMS). A conceptual framework for adaptive learning systems is proposed for this purpose.
The framework is based on the idea that adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of learners and the content type of learning material are used to match the learner to the most suitable content.
The thesis covers the pedagogical framework applied in MODA, the technical and multi-agent architectures of MODA, the TCP-IP based protocol providing communication between MODA and LMS, and a sample application of the system to an open source learning management system, OLAT. The study also discusses the possibilities of future interests.
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Qela, Blerim. "Adaptive Systems for Smart Buildings Utilizing Wireless Sensor Networks and Artificial Intelligence." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20553.
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In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest.
A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.
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Lindkvist, Emilie. "Learning-by-modeling : Novel Computational Approaches for Exploring the Dynamics of Learning and Self-governance in Social-ecological Systems." Doctoral thesis, Stockholms universitet, Stockholm Resilience Centre, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122395.
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As a consequence of global environmental change, sustainable management and governance of natural resources face critical challenges, such as dealing with non-linear dynamics, increased resource variability, and uncertainty. This thesis seeks to address some of these challenges by using simulation models. The first line of research focuses on the use of learning-by-doing (LBD) for managing a renewable resource, exemplified by a fish stock, and explores LBD in a theoretical model using artificial intelligence (Paper I and II). The second line of research focuses on the emergence of different forms of self-governance and their interrelation with the dynamics of trust among fishers when harvesting a shared resource, using an agent-based model. This model is informed by qualitative data based on small-scale fisheries in Mexico (Paper III and IV). Paper I and II find that the most sustainable harvesting strategy requires that the actor values current and future yields equally, cautiously experiments around what is perceived as the best harvest action, and rapidly updates its ‘mental model’ to any perceived change in catch. More specifically, Paper II reveals that understanding these aspects in relation to the type of change can yield not only increased performance, but also, and more importantly, increased robustness to both fast and slow changes in resource dynamics. However, when resource dynamics include the possibility of a more fundamental shift in system characteristics (a regime shift), LBD is problematic due to the potential for crossing a threshold, resulting in possible persistent reductions in harvests (Paper I). In Paper III, results indicate that cooperative forms of self-governance are more likely to establish and persist in communities where fishers’ have prior cooperative experience, fishers’ trustworthiness is more or less equal, and that this likelihood increases when resource availability fluctuates seasonally. Finally, to achieve a transformation toward more cooperative forms of self-governance, interventions are required that can strengthen both financial capital and trust among the members of the cooperatives (Paper IV). The unique contribution of this thesis lies in the method for ‘quantitatively’ studying LBD, the stylized model of a small-scale fishery, and the analysis of the two models to advance our understanding of processes of learning and self-governance in uncertain and variable social-ecological environments. Together, the results shed light on how social and ecological factors and processes co-evolve to shape social-ecological outcomes, as well as contributing to the development of novel methods within the emerging field of sustainability science.<br>I vårt antropocena tidevarv är ett långsiktigt förvaltarskap av naturresurser inom social-ekologiska system av yttersta vikt. Detta kräver en djup förståelse av människan, ekologin, interaktionerna sinsemellan och deras utveckling över tid. Syftet med denna avhandling är att nå en djupare och mer nyanserad förståelse kring två av grundpelarna inom forskningen av hållbar förvaltning av naturresurser–kontinuerligt lärande genom learning-by-doing (LBD) för att förstå naturresursens dynamik, samt vad som kan kallas socialt kapital, i detta sammanhang i betydelsen tillit mellan individer, som naturligtvis ligger till grund för framgångsrik gemensam förvaltning. Denna föresats operationaliseras genom att använda två olika simuleringsmodeller. Den ena modellen undersöker hur en hållbar förvaltning av en förnyelsebar resurs, i denna avhandling exemplifierad av en fiskepopulation, kan uppnås genom LBD. Den andra modellen söker blottlägga det komplexa sociala samspel som krävs för att praktisera gemensam förvaltning genom att använda ett fiskesamhälle som fallstudie. Tidigare forskning på båda dessa två områden är relativt omfattade. Emellertid har den forskning som specialiserat sig på LBD i huvudsak inskränkt sig till empiriska fallstudier. Vad som bryter ny mark i denna avhandling är att vi konstruerar en simuleringsmodell av LBD där vi kan studera lärandeprocessen i detalj för att uppnå en mer hållbar förvaltning över tid. Beträffande modellen som behandlar socialt kapital så har tidigare forskning fokuserat på hur en organisation, eller grupp, kan uppnå hållbar förvaltning. Dock saknas ett helhetsgrepp där som tar hänsyn till alla nivåer; från individnivå (mikro), via gruppnivå (meso), till samhällsnivå (makro). Detta är något som denna avhandling försöker avhjälpa genom att undersöka betydelsen av individers egenskaper, uppbyggnaden av socialt kapital, samt hur detta påverkar emergens av ett samhälle dominerat av mer kooperativa förvaltningsformer respektive mer hierarkiska diton. I papper I and II studeras kärnan av LBD som återkoppling mellan en aktör och en resurs, där aktören lär sig genom upprepade interaktioner med en resurs. Resultaten visar att LBD är av avgörande betydelse för en hållbar förvaltning, speciellt då naturresursens dynamik är stadd i förändring. I den mest hållbara strategin bör aktören värdera nuvarande och framtida fångster lika högt, försiktigt experimentera kring vad aktören upplever som bästa strategi, för att sedan anpassa sin mentala modell till upplevda förändringar i fångst relativt dess insats någorlunda kraftigt. I papper III och IV behandlas uppbyggnaden av förtroende mellan individer och grupp, samt själv-organiserat styre. Genom att använda småskaligt fiske i Mexiko som en illustrativ fallstudie, utvecklades en agent-baserad modell av ett arketypiskt småskaligt fiskesamhälle. Resultaten indikerar att kooperativa förvaltningsformer är mer dominanta i samhällen där de som utför fisket har liknande pålitlighet, starkt gemensamt socialt kapital vid kooperativets start, och då resursen fluktuerar säsongsmässigt (papper III). Papper IV visar att för att uppnå en transformation från hierarkiska förvaltningsformer till kooperativa diton krävs interventioner som inriktar sig på både socialt och finansiellt kapital. Denna avhandling bidrar således till en djupare förståelse kring hur socialt kapital växer fram, samt hur mer strategiska LBD processer bör utformas när abrupta och osäkra förändringar i ekosystemen blir allt vanligare på grund av människans ökade tryck på planeten.<br><p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 3: Submitted. Paper 4: Manuscript.</p>
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Kocabas, Efe Cem. "Uml-alf Agent Based Adaptive Learning Framework:a Case Study On Uml." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612182/index.pdf.
Full textAbstract:
As the amount of accessible and shareable knowledge increases, it is figured out that learning platforms offering the same context and learning path to all users can not meet the
demands of learners. This issue brings out the necessity of designing and developing adaptive hypermedia systems. This study describes an agent-based adaptive learning
framework whose goal is to implement effective tutoring system with the help of Artificial Intelligence (AI) techniques and cognitive didactic methods into Adaptive Educational
Hypermedia Systems (AEHS) in the domain of Unified Modeling Language (UML). There are three main goals of this study. First goal is to explore how supportive agents affect
student&rsquo<br>s learning achievement in distance learning. Second goal is to examine the interaction between supportive agents and learners with the help of experiments in Human
Computer Interaction laboratories and system analysis. The effects of the methodology that agents give misleading hints which are common mistakes of other learners are also
investigated. Last goal is to deliver effective feedback to students both from IAs and tutors.
In order to assess that UML-ALF has accomplished its objectives, we followed an experimental procedure. Experimental groups have taken the advantage of adaptive and
intelligent techniques of the UML-ALF and control groups have used the traditional learning techniques. The results show that there is a positive correlation between variables practice
score and number of agent suggestion which means, as the participants benefit from supportive agents, they get higher scores.
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Wang, Yu. "Adaptive control and learning using multiple models." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10783473.
Full textAbstract:
<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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Wang, Olivier. "Adaptive Rules Model : Statistical Learning for Rule-Based Systems." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX037/document.
Full textAbstract:
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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Mani, Kashani Mina. "The carbon cycle and systems thinking : Conceptualizing a visualization-based learning system for teaching the carbon cycle that supports systems thinking." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177716.
Full textAbstract:
Today, climate change, has become one of the greatest societal challenges of our time. This challenge requires an accurate understanding of climate change for making informed decisions regarding the environmental issues. The carbon cycle is one of the earth’s complicated cycles that has a critical role in the planet’s climate. Developing a thorough perception about this complex cycle uncovers how human activities impact the planet and reveals the connection between multiple environmental issues.Perceiving this complex cycle requires systems thinking skills that enable students to recognize components of the carbon cycle and understand the interrelating dynamic relationship between them. Establishing systems thinking skills and developing a thorough perception about the carbon cycle is a difficult matter for students. Adaptive visualisation-based tutoring systems have a great potential for facilitating teaching and learning cyclical models and systems thinking in schools. Such systems consider the students’ needs and provide personalised feedback that can guide individuals more effectively throughout the learning process. This thesis project intends to use diagrammatic visualizations, systems thinking, and adaptive tutoring systems as three technical approaches for conceptualising a learning system that aims to teach the carbon cycle. The framework of this thesis project is formed in relation to a research project called ‘Tracing Carbon’ focusing on science education for pupils on grade 7-9.
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Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.
Full textAbstract:
Les ordinateurs peuvent-ils enseigner ? Pour répondre à cette question, la recherche dans les Systèmes Tuteurs Intelligents est en pleine expansion parmi la communauté travaillant sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE). C'est un domaine qui rassemble différentes problématiques et réunit des chercheurs venant de domaines variés, tels que la psychologie, la didactique, les neurosciences et, plus particulièrement, le machine learning. Les technologies numériques deviennent de plus en plus présentes dans la vie quotidienne avec le développement des tablettes et des smartphones. Il semble naturel d'utiliser ces technologies dans un but éducatif. Cela amène de nombreuses problématiques, telles que comment faire des interfaces accessibles à tous, comment rendre des contenus pédagogiques motivants ou encore comment personnaliser les activités afin d'adapter le contenu à chacun. Au cours de cette thèse, nous avons développé des méthodes, regroupées dans un framework nommé HMABITS, afin d'adapter des séquences d'activités pédagogiques en fonction des performances et des préférences des apprenants, dans le but de maximiser leur vitesse d'apprentissage et leur motivation. Ces méthodes utilisent des modèles computationnels de motivation intrinsèque pour identifier les activités offrant les plus grands progrès d'apprentissage, et utilisent des algorithmes de Bandits Multi-Bras pour gérer le compromis exploration/exploitation à l'intérieur de l'espace d'activité. Les activités présentant un intérêt optimal sont ainsi privilégiées afin de maintenir l'apprenant dans un état de Flow ou dans sa Zone de Développement Proximal. De plus, certaines de nos méthodes permettent à l'apprenant de faire des choix sur des caractéristiques contextuelles ou le contenu pédagogique de l'application, ce qui est un vecteur d'autodétermination et de motivation. Afin d'évaluer l'efficacité et la pertinence de nos algorithmes, nous avons mené plusieurs types d'expérimentation. Nos méthodes ont d'abord été testées en simulation afin d'évaluer leur fonctionnement avant de les utiliser dans d'actuelles applications d'apprentissage. Pour ce faire, nous avons développé différents modèles d'apprenants, afin de pouvoir éprouver nos méthodes selon différentes approches, un modèle d'apprenant virtuel ne reflétant jamais le comportement d'un apprenant réel. Les résultats des simulations montrent que le framework HMABITS permet d'obtenir des résultats d'apprentissage comparables et, dans certains cas, meilleurs qu'une solution optimale ou qu'une séquence experte. Nous avons ensuite développé notre propre scénario pédagogique et notre propre serious game afin de tester nos algorithmes en situation réelle avec de vrais élèves. Nous avons donc développé un jeu sur la thématique de la décomposition des nombres, au travers de la manipulation de la monnaie, pour les enfants de 6 à 8 ans. Nous avons ensuite travaillé avec le rectorat et différentes écoles de l'académie de bordeaux. Sur l'ensemble des expérimentations, environ 1000 élèves ont travaillé sur l'application sur tablette. Les résultats des études en situation réelle montrent que le framework HMABITS permet aux élèves d'accéder à des activités plus diverses et plus difficiles, d'avoir un meilleure apprentissage et d'être plus motivés qu'avec une séquence experte. Les résultats montrent même que ces effets sont encore plus marqués lorsque les élèves ont la possibilité de faire des choix<br>Can computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices
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Gonçalves, Diogo Antunes. "Energy management systems based on adaptive surrogate modelling." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23559.
Full textAbstract:
Doutoramento em Sistemas Energéticos e Alterações Climáticas<br>Estima-se que o sector dos edifícios seja responsável por cerca de 40%
da totalidade de energia consumida na União Europeia e Estados Unidos
da América. 50% dessa energia está alocada a sistemas de aquecimento,
ventilação e ar-condicionado (AVAC), dos quais 20% estimam-se ser desperdiçados devido a ineficiência na gestão de energia. Considera-se pertinente
focar-se no melhoramento da eficiência energética do edificado, reduzindo
o desperdício de forma a evitar a escassez de recursos fósseis, bem
como para mitigar os problemas ambientais e as alterações climáticas causadas
pelo consumo e produção de energia.
A tese propõe abordagens e metodologias que permitem tomar o controlo
preditivo de supervisão dos sistemas de climatização enquanto medida de reabilitação energética na requalificação de edifícios. A principal contribuição
deste trabalho prende-se com a implementação e desenvolvimento de metamodelos
adaptativos baseados em aprendizagem computacional que assistam
o processo de otimização multi-objetivo inerente ao controlo supervisor
da gestão de energia em edifícios de serviços. Esta metodologia deverá
ainda permitir a sua implementação de forma agnóstica a natureza dos sistemas
AVAC existentes no edifício.
A metodologia apresentada propõe uma abordagem convergente com o estado
da arte no desenvolvimento científico na área da inteligência artificial.
O esforço mínimo requerido para a implementação deste tipo de sistema de
gestão inteligente e avaliado, concluindo-se que o seu potencial de aplicação
e significativo. Para este fim, foi desenvolvida uma aplicação informática
capaz de conduzir toda a metodologia em regime de simulação computacional
de modo a averiguar a utilidade das soluções propostas pelo sistema
de controlo supervisor desenvolvido.
Os resultados obtidos apresentam soluções compatíveis com o melhoramento
do paradigma energético-ambiental corrente, contribuindo desse
modo para uma maior sustentabilidade do edificado obsoleto em termos energéticos. Os custos com energia alocada a sistemas AVAC podem alcançar
uma redução de 27% dos custos base, acompanhando uma melhoria ao nível
do conforto dos ocupantes. Mesmo em casos em que a requalificação da
envolvente do edifício e do sistema de climatização seja anterior a implementação de um sistema de gestão inteligente, ou que a envolvente seja
já competente em termos de eficiência energética (como o caso de estudo
apresentado), a poupança energética e, ainda assim, assegurada devido a
natureza
flexível e autodidata do sistema de supervisão proposto. Portanto,
recomenda-se que a reabilitação energética de edifícios tome como prioridade
a requalificação do sistema de controlo AVAC por sistemas avançados
e supervisores de controlo de forma a potenciarem a inércia dos edifícios,
bem como toda a informação disponível na atual era digital.<br>Buildings account for almost 40% of the total energy consumption in the
European Union and the United States combined. From that fraction, 50%
is allocated to the heating, ventilation and air-conditioning systems (HVAC),
from which 20% is wasted due to system's ine ciency. Considering that
most of this energy is obtained from scarce fossil reserves and its consumption
has an adverse impact on the climate change problem, it is of utmost
importance to reduce energy wastes, namely by improving the overall energy
e ciency of buildings.
This thesis postulates the implementation of intelligent supervisory control
systems for new or existing HVAC equipment as an energy retro tting measure
concurrent with conventional architectural and systems retro tting.
The proposed methodology is characterized by a
exible, yet robust predictive
control algorithm, capable of supervising generic HVAC systems in
real-time by suggesting high-level controls, resulting in an optimized compromise
between occupants' comfort requirements and energy consumption
(and/or cost), taking advantage of the building constructive characteristics
and information availability.
The proposed solution integrates the
exibility of machine learning techniques
with the robustness of surrogate models to deliver data-driven predictive
models capable of assisting the multi-objective optimization problem
of minimizing energy consumption and cost while improving occupants comfort.
The proposed modelling and optimization strategies are presented as a
novelty capable of answering the quest for a robust yet
exible supervisory
predictive control for generic HVAC systems. A software package capable of
delivering advanced and generic supervisory predictive controls, especially
focusing on the scope of building energy retro tting was developed and used
as the delivery method for the results presented in this thesis.
The obtained results suggest that o ce buildings, characterized by a contemporary
construction and HVAC system, can be improved regarding overall
energy e ciency and occupants comfort by retro tting the control solution
adding a supervisory predictive control level, external to the existing
HVAC system. The expected energy saving by considering the proposed
control are in line with the requirements imposed by the present energy
and climate change framework, with up to 27% savings of energy related
costs due to autonomous demand shifting. Moreover, it is recommended
that building energy retro ts should consider as a priority the update of the
energy control strategies by adding supervisory solutions capable of capitalizing
the use of the building thermal inertia as well as the available data in
this current information era (occupancy schedules, weather, etc.).
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Chowdhary, Girish. "Concurrent learning for convergence in adaptive control without persistency of excitation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37243.
Full textAbstract:
Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command.
This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems.
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Asri, Layla El. "Learning the Parameters of Reinforcement Learning from Data for Adaptive Spoken Dialogue Systems." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0350/document.
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Cette thèse s’inscrit dans le cadre de la recherche sur les systèmes de dialogue. Ce document propose d’apprendre le comportement d’un système à partir d’un ensemble de dialogues annotés. Le système apprend un comportement optimal via l’apprentissage par renforcement. Nous montrons qu’il n’est pas nécessaire de définir une représentation de l’espace d’état ni une fonction de récompense. En effet, ces deux paramètres peuvent être appris à partir du corpus de dialogues annotés. Nous montrons qu’il est possible pour un développeur de systèmes de dialogue d’optimiser la gestion du dialogue en définissant seulement la logique du dialogue ainsi qu’un critère à maximiser (par exemple, la satisfaction utilisateur). La première étape de la méthodologie que nous proposons consiste à prendre en compte un certain nombre de paramètres de dialogue afin de construire une représentation de l’espace d’état permettant d’optimiser le critère spécifié par le développeur. Par exemple, si le critère choisi est la satisfaction utilisateur, il est alors important d’inclure dans la représentation des paramètres tels que la durée du dialogue et le score de confiance de la reconnaissance vocale. L’espace d’état est modélisé par une mémoire sparse distribuée. Notre modèle, Genetic Sparse Distributed Memory for Reinforcement Learning (GSDMRL), permet de prendre en compte de nombreux paramètres de dialogue et de sélectionner ceux qui sont importants pour l’apprentissage par évolution génétique. L’espace d’état résultant ainsi que le comportement appris par le système sont aisément interprétables. Dans un second temps, les dialogues annotés servent à apprendre une fonction de récompense qui apprend au système à optimiser le critère donné par le développeur. A cet effet, nous proposons deux algorithmes, reward shaping et distance minimisation. Ces deux méthodes interprètent le critère à optimiser comme étant la récompense globale pour chaque dialogue. Nous comparons ces deux fonctions sur un ensemble de dialogues simulés et nous montrons que l’apprentissage est plus rapide avec ces fonctions qu’en utilisant directement le critère comme récompense finale. Nous avons développé un système de dialogue dédié à la prise de rendez-vous et nous avons collecté un corpus de dialogues annotés avec ce système. Ce corpus permet d’illustrer la capacité de mise à l’échelle de la représentation de l’espace d’état GSDMRL et constitue un bon exemple de système industriel sur lequel la méthodologie que nous proposons pourrait être appliquée<br>This document proposes to learn the behaviour of the dialogue manager of a spoken dialogue system from a set of rated dialogues. This learning is performed through reinforcement learning. Our method does not require the definition of a representation of the state space nor a reward function. These two high-level parameters are learnt from the corpus of rated dialogues. It is shown that the spoken dialogue designer can optimise dialogue management by simply defining the dialogue logic and a criterion to maximise (e.g user satisfaction). The methodology suggested in this thesis first considers the dialogue parameters that are necessary to compute a representation of the state space relevant for the criterion to be maximized. For instance, if the chosen criterion is user satisfaction then it is important to account for parameters such as dialogue duration and the average speech recognition confidence score. The state space is represented as a sparse distributed memory. The Genetic Sparse Distributed Memory for Reinforcement Learning (GSDMRL) accommodates many dialogue parameters and selects the parameters which are the most important for learning through genetic evolution. The resulting state space and the policy learnt on it are easily interpretable by the system designer. Secondly, the rated dialogues are used to learn a reward function which teaches the system to optimise the criterion. Two algorithms, reward shaping and distance minimisation are proposed to learn the reward function. These two algorithms consider the criterion to be the return for the entire dialogue. These functions are discussed and compared on simulated dialogues and it is shown that the resulting functions enable faster learning than using the criterion directly as the final reward. A spoken dialogue system for appointment scheduling was designed during this thesis, based on previous systems, and a corpus of rated dialogues with this system were collected. This corpus illustrates the scaling capability of the state space representation and is a good example of an industrial spoken dialogue system upon which the methodology could be applied
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Vegah, Godwill. "Software agents support for personalised learning: Negotiating and e-contracting with multiple providers." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/software-agents-support-for-personalised-learningnegotiating-and-econtracting-with-multiple-providers(b8aed1dc-2ef8-4458-99d3-f935118fa87b).html.
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E-learning is increasingly adopted to support face-to-face classroom-based learning or implemented as a complete standalone learning system. Its inherent adaptable nature and ability to provide learning anywhere, everywhere and anytime makes it a versatile tool for access to basic, professional and higher education. This research proposes and develops an adaptable e-learning approach, focusing on the learner's requirement specification and negotiation of course with multiple providers to improve online learning. This addresses issues of inflexible learning model, narrow coverage of subject domains in existing systems and ineffective use of educational resources, using design research methodology (DRM). The proposed Intelligent Learning approach provides learning support by applying collaborative and deliberative capabilities of software agents to e-learning systems. Designated learning support agents negotiate with providers on behalf of the learner for courses, matching specified requirements. This is achieved through agent negotiation strategies, devising dynamic learning plans (DPLAN) and online learning contract (or EContract) between the system and a range of providers, to harness the changing needs of the learner, hence, providing an Adaptive Agent Learner Plan (ADALP) approach. It develops and applies a 'Basic Requirements Learning' model, addressing specific learning objectives, supported by a two way evaluation process that enforces learning flexibility, empowering learners and accommodating a wide spectrum of learning needs. Unlike traditional Intelligent Tutoring System (ITS), learning objectives are not fixed and are constituted dynamically from learner specifications. The ADALP approach provides multiple provider support options, generating learner feedback for goal oriented, but flexible learning. This deviates from the traditional 'top-down' approach, where instructors and designers create fixed models of different categories of learners and their needs. The prototype of multi-agent system (MAS) demonstrates contributions of the approach, applying Multi-issue-Negotiation and Contracting Courses with Multiple Providers; devising dynamic personalised learning plans and learning commitment (or e-contracts) between learners and providers. It implements designated agents which generate tasks and sub-tasks corresponding to the learners' goals and objectives; 'biding' for learning and tutoring resources from multiple providers to deliver on the derived tasks. Personalised learning plan aligned with online learning contract is generated for each learner based on the specified requirements and learning goals, as a result. It is argued that the ADALP approach empowers learners and improves on similar approaches, in comparison to existing adaptive learning systems.
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Belhumeur, Corey T. "An Empirical Evaluation of Student Learning by the Use of a Computer Adaptive System." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/223.
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Numerous methods to assess student knowledge are present throughout every step of a students€™ education. Skill-based assessments include homework, quizzes and tests while curriculum exams comprise of the SAT and GRE. The latter assessments provide an indication as to how well a student has retained a learned national curriculum however they are unable to identify how well a student performs at a fine grain skill level. The former assessments hone in on a specific skill or set of skills, however, they require an excessive amount of time to collect curriculum-wide data. We've developed a system that assesses students at a fine grain level in order to identify non- mastered skills within each student€™s zone of proximal development. €œPLACEments€� is a graph-driven computer adaptive test which not only provides thorough student feedback to educators but also delivers a personalized remediation plan to each student based on his or her identified non-mastered skills. As opposed to predicting state test scores, PLACEments objective is to personalize learning for students and encourage teachers to employ formative assessment techniques in the classroom. We have conducted a randomized controlled study to evaluate the learning value PLACEments provides in comparison to traditional methods of targeted skill mastery and retention.
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Chang, Long. "Implementation of Improved AIRS with Adaptive Online Learning Capability for Cloud-enabled Fault Detection and Diagnosis of HVAC Systems in Intelligent Buildings." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1417370136.
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Hrubik-Vulanovic, Tatjana. "EFFECTS OF INTELLIGENT TUTORING SYSTEMS IN BASIC ALGEBRA COURSES ON SUBSEQUENT MATHEMATICS LECTURE COURSES." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1373337020.
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Chao, Zenas C. "Toward the neurocomputer goal-directed learning in embodied cultured networks/." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19816.
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Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Potter, Steve; Committee Member: Butera, Robert; Committee Member: DeMarse, Thomas; Committee Member: Jaeger, Dieter; Committee Member: Lee, Robert.
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Kalantari, John I. "A general purpose artificial intelligence framework for the analysis of complex biological systems." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5953.
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This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.
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Oliveira, Ivan Carlos Alcântara de. "AdaptMLearning: uma proposta de sistema de aprendizagem adaptativo e inteligente." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26122013-150826/.
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Sistemas de Aprendizagem Adaptativos e Inteligentes, tema de pesquisa recente no mundo, são ambientes com arquitetura e algoritmos específicos, que consideram as características individuais de cada estudante para selecionar o objeto de aprendizagem mais adequado a ser oferecido ao aluno. O rápido desenvolvimento da infraestrutura sem fio e o amplo uso de dispositivos móveis na vida diária das pessoas motivam as pesquisas relativas ao uso desses dispositivos na educação, proporcionando o m-learning. Assim, relacionado a essas linhas de pesquisa, este trabalho propõe a arquitetura AdaptMLearning, elaborada para prover a aprendizagem em plataformas móveis e não móveis, considerando a seleção de objetos de aprendizagem que melhor se adaptam a diversos aspectos, tais como: dados sobre a tecnologia utilizada para acesso; informações sobre o estilo de aprendizagem de um estudante; desempenho e tempo associados à interação do estudante com o objeto de aprendizagem; conhecimentos adquiridos pelo estudante em consonância ao conteúdo do curso; e a garantia de que não só o professor possa configurar as adaptações a serem oferecidas ao seu curso, como também o aluno tenha a possibilidade de informar sua preferência pelos tipos de mídia. Essa arquitetura é baseada no modelo de referência AHAM para sistemas adaptativos AEHS, contemplando a quádrupla: espaço do conhecimento, modelo do usuário, observações e modelo de adaptação, referente à definição lógica desses sistemas. Na AdaptMLearning, foram desenvolvidos alguns algoritmos, utilizando-se o modelo FSLSM, relacionado aos estilos de aprendizagem de um estudante e o padrão IEEE 1484 para catalogação dos objetos de aprendizagem e uso de alguns atributos de suas categorias, associados às dimensões dos estilos de aprendizagem do modelo FSLSM. O algoritmo calcula um peso para um objeto catalogado em cada dimensão e permite uma busca pelo objeto mais adequado ao estilo do estudante, além de usar a computação fuzzy, para avaliar se o estudante pode sofrer mudanças no seu estilo, deve receber reforço ou necessita de um reestudo em determinado assunto de um curso, por meio de resultados obtidos com o tempo de estudo e desempenho. Também, este trabalho apresenta o desenvolvimento e a avaliação de um simulador para a arquitetura AdaptMLearning e seus algoritmos, realizada utilizando diversos cenários de simulação, envolvendo estudantes, cursos e tecnologias com diferentes configurações. Assim sendo, com base nos resultados obtidos por meio da avaliação, foi possível discutir, analisar e identificar o potencial de uso da AdaptMLearning e de seus algoritmos em uma situação real para elaboração de um ambiente de aprendizagem ou agregação a um ambiente existente.<br>Intelligent and Adaptive Learning Systems, subject of recent research in the world, are environments with specific architectures and algorithms, designed considering the individual characteristics of each student. The rapid development of wireless infrastructures and wide use of mobile devices in people\'s everyday life encourage research about the use of these devices in education, providing the mlearning. In the context of such research, this work proposes the AdaptMLearning architecture that was designed to be a learning infrastructure for mobile and nonmobile platforms. This architecture provides a selection of learning objects that takes into account as adaptation criteria the following data: the mobile device\'s technological specification; the student\'s learning style information, his/her performance and spent time associated to the student\'s interaction with the learning object; previously acquired knowledge by the student related to the course\'s content. In addition, it also allows the teacher to interfere in the adaptation criteria used during the study simulation, and allows the student to indicate his/her preferences for media types. This architecture is based on AHAM reference model for adaptive systems AEHS and uses the quadruple: the knowledge space, the user model, the observations and the model adaptation, referring to the logical definition of these systems. To implement the AdaptMLearning architecture some algorithms using the FSLSM model related to the student\'s learning styles were developed. The algorithms use the IEEE 1484 for cataloging learning objects and some of its categories and attributes associated with dimensions of learning styles FSLSM model, are used to compute a weight of an object in each dimension allowing a search of the most appropriate object according to the student\'s learning styles; and the use of fuzzy computing, considering that the student\'s learning style can change, determines if the student has to receive reinforcement or need a new study in a particular subject of a course, when the student gets unsatisfactory results in terms of timing and performance in a course\'s subject. Also, this work also presents the development and evaluation of a simulator for the AdaptMLearning architecture and their algorithms. The evaluation of the simulator was done by means of many simulations scenarios, considering students, courses and technologies with different settings. Based on the results obtained from the evaluation it was possible to discuss, analyze and identify the potential use of AdaptMLearning architecture and their algorithms in a real situation for developing a learning environment or its aggregation to an existing environment.
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Reis, Alessandro Boeira dos. "Um modelo do aluno adaptivo para sistemas na web." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2001. http://hdl.handle.net/10183/10536.
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Esta dissertação de mestrado está inserida no trabalho desenvolvido pelo grupo de pesquisa GIA/UFRGS e situa-se na área de Inteligência Artificial aplicada à Educação à Distância apresentando uma arquitetura distribuída no desenvolvimento de Ambientes de Ensino Inteligentes. A Web facilita aos alunos encontrarem informações relevantes ao estudo que estão fazendo. Outra vantagem do ensino na Web é o indivíduo ser sabedor da existência da informação e de onde ela se localiza para que no momento adequado a acesse. Porém, o progresso que se poderá ter na área de informática aplicada à educação dependerá da adequação e qualidade do software educacional a ser utilizado. Por isso, temos que ter software de ensino inteligentes, que possam trazer esta qualidade e adequação conforme o aluno que está tentando adquirir um certo conhecimento. O objetivo principal do presente trabalho se propõe a estudar as diferentes técnicas e mecanismos de se construir um modelo do aluno adaptativo na Web, aplicáveis em um cenário de educação à distância, a partir de um ambiente distribuído de ensino-aprendizagem inteligente baseado em uma arquitetura multiagentes que contempla o paradigma de ensino cooperativo (uma sociedade de agentes humanos e artificiais, que cooperam para alcançar um objetivo comum). Além disso, o trabalho propõe o uso de estratégias de ensino segundo regras estipuladas pelo especialista. O agente responsável por este controle observa as modificações do comportamento do aprendiz durante a sua interação no ambiente, e seleciona uma estratégia de acordo com o desempenho do aprendiz.<br>This master’s degree dissertation was developed within the research group GIA/UFRGS, and is located in the field of Artificial Intelligence applied to distance learning, presenting a distributed architecture to develop intelligent teaching environments. The Web make easier for students to find important information for the study hey are performing. Another advantage of learning on the Web is that one knows the information exist and where they are located in order to access it at the right moment. However, the advances in the area of learning applied computing will depend of the adaptation and quality of the educational software used. Thus, we need intelligent teaching software, which can bring us this quality and adaptation according to the student that is trying to acquire some knowledge. The main goal of this work is to propose to study the different techniques and mechanisms for building an adaptive student model on the Web, that are applicable in a distance learning scenario, from an intelligent teaching-learning distributed environment based in a multi-agent architecture that contemplate the cooperative teaching paradigm (a society of human and artificial agent, which cooperate to reach a common goal). In addition, this work proposes the use of teaching strategies according to rules given by the specialist. The agent responsible for this control observes the apprentice behavioral changes during his interaction with the environment and selects a strategy according to the apprentice performance.
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Thompson, Allan. "Adaptive intelligent tutoring systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22783.pdf.
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Silva, J?lio C?sar da Costa. "Detec??o Autom?tica e Din?mica de Estilos de Aprendizagem em Sistemas Adaptativos e Inteligentes utilizando Dynamic Scripting." UFVJM, 2017. http://acervo.ufvjm.edu.br/jspui/handle/1/1648.
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?rea de concentra??o: Educa??o e Tecnologias aplicadas em Institui??es Educacionais.<br>Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-04-12T14:17:26Z
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Previous issue date: 2017<br>Uma das formas de se gerar conte?do adaptado ao estudante passa, primeiro, pela detec??o
dos Estilos de Aprendizagem (EA). A teoria dos EA presume que cada aluno tem caracter?sticas
pr?prias que o distingue dos demais. A partir dos EA, o Sistema Adaptativo e
Inteligente para Educa??o (SAIE) de Dor?a foi idealizado. Seu trabalho objetiva apresentar
uma solu??o estoc?stica para provimento de adaptatividade e customiza??o de Sistemas
Educacionais por meio da modelagem probabil?stica dos EA. Em s?ntese, seu SAIE visa
modelar o estudante, coletando e atualizando seus dados, de forma a descobrir seu EA.
Com este fim, o sistema, durante suas itera??es, submete o aluno a avalia??es e, caso as
notas sejam insatisfat?rias, o sistema realiza a atualiza??o do modelo do estudante (ME)
por meio do Aprendizado por Refor?o (AR). Contudo, AR ? considerada uma t?cnica lenta
de aprendizado que demanda muito tempo para ajustar o elemento a ser otimizado. Por
sua vez, a t?cnica Dynamic Scripting (DS), uma varia??o da t?cnica de AR, apresenta alta
velocidade de converg?ncia, mesmo em ambientes din?micos. DS ? popularmente utilizada
na IA de Jogos e consiste em um conjunto de Regras sobre um dom?nio, estruturadas
por uma condi??o e uma a??o. Sua forma de aprendizagem atrela um peso a cada regra,
o qual determina a qualidade da regra, frente ? sua condi??o, e uma probabilidade da
mesma ser aplicada. A condi??o de uma regra ? a representa??o de uma situa??o poss?vel
no sistema, e sua a??o ? a interven??o gerada no sistema durante a sua aplica??o. Este
trabalho prop?e o aperfei?oamento do SAIE citado, utilizando uma adapta??o do DS, com
os objetivos de acelerar a converg?ncia do sistema, reduzir os Problemas de Aprendizagem
(PA) e aumentar a nota do estudante. Adicionalmente, devido a caracter?stica din?mica do
DS, este trabalho realiza experimentos em situa??es em que o EA Real (EAr) dos alunos
variam ao longo do processo de ensino/aprendizagem. A pesquisa parte da elabora??o das
regras e implementa??o da estrutura do DS, avan?ando para a substitui??o do m?dulo
de AR pelo DS no SAIE de Dor?a. Realizaram-se 30 testes para cada uma das 16 Combina??es
de EA (CEA), 16*30 testes para cada uma das 4 abordagens: Dor?a-Est?tico,
Dor?a-Din?mico, DS-Est?tico e DS-Din?mico. Nos testes din?micos, modificou-se o EAr a
cada 150 intera??es, de forma que ap?s 300 intera??es, o sistema deve convergir para uma
CEA oposta ? inicial. Resultados preliminares, em compara??o ? abordagem da literatura,
apresentaram uma redu??o m?dia nos PA de 35.8% para os testes din?micos e de 54.1%
para os testes est?ticos. Quando o EA Probabil?stico (EAp) inicial ? exatamente igual ao
EAr, verificou-se que a abordagem proposta apresentou em m?dia 6 erros na atualiza??o
do ME, enquanto a abordagem da literatura apresentou, em m?dia, 23 erros. Verificou-se,
portanto, que, preliminarmente, a proposta obteve resultados promissores.<br>Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.<br>One of the ways to generate the content adapted to the student passes, first, by the
detection of Learning Styles (LS). The LS theory assumes that every time you have
distances. From the LS, the Adaptive and Intelligent System for Education (AISE) of
Dor?a was idealized. His work aims to present a stochastic solution for the provision of
adaptability and customization of Educational Systems through the probabilistic modeling
of LS. In summary, your AISE visa model, offering and updating your data, in order to
discover your LS. To this end, the system, during its iterations, submits the student to
the evaluation and, in case of notes and dissatisfactions, the system performs an updating
of the student model (ME) through Reinforcement Learning (RL). However, RL is a slow
learning technique that requires a lot of time to adjust the element to be optimized. In
turn, a Dynamic Scripting (DS) technique, a variation of the RL technique, presents a high
speed of convergence, even in dynamic environments. DS is popularly used in Artificial
Intelligence of Games and consists of a set of Rules on a domain, structured by a condition
and an action. Its form of learning brings a weight to each rule, which determines a quality
of the rule, in front of its condition, and a probability of the same company. The condition
of a rule is a representation of a good situation, and its action is an intervention generated
without system during its application. This work proposes the improvement of the SAIE
mentioned, the use of an adaptation of the DS, with the objectives of accelerating the
convergence of the system, reduce the Learning Problems (PA) and increase student grade.
In addition, due to the dynamic nature of the DS, this work performs tasks in situations in
which students? real LS (LSr) vary throughout the teaching / learning process. A research
of elaboration of the rules and implementation of the structure of the DS, advancing to
the substitution of the RL module by the DS without AISE of Dor?a. A total of 30 tests
were performed for each of the 16 AE combinations (CEA), 16 * 30 testicles for each
of the 4 approaches: Dorca-Static, Dynamic Doric, DS-Static and DS-Dynamic. In the
dynamic tests, the LSr was modified every 150 interactions, so that after 300 interactions,
the system must converge to a CEA opposite to the initial one. Preliminary results, in
literature comparison, presented a mean reduction in BP of 35.8 % for dynamic tests and
54.1 % for static tests. When the initial Probabilistic LS (LSp) is exactly the same as
the LSr, it was verified that the proposed approach presented on average 6 errors in the
updating of the ME, while a literature approach presented, on average, 23 errors. It was
therefore found that a proposal had obtained promising results in the first place.
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Sridharan, Aswinkumar. "Adaptive and intelligent memory systems." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S140/document.
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Dans cette thèse, nous nous sommes concentrés sur l'interférence aux ressources de la hiérarchie de la mémoire partagée : cache de dernier niveau et accès à la mémoire hors-puce dans le contexte des systèmes multicœurs à grande échelle. À cette fin, le premier travail a porté sur les caches de dernier niveau partagées, où le nombre d'applications partageant le cache pourrait dépasser l'associativité du cache. Pour gérer les caches dans de telles situations, notre solution évalue l'empreinte du cache des applications pour déterminer approximativement à quel point elles pourraient utiliser le cache. L'estimation quantitative de l'utilitaire de cache permet explicitement de faire respecter différentes priorités entre les applications. La seconde partie apporte une prédétection dans la gestion de la mémoire cache. En particulier, nous observons les blocs cache pré-sélectionnés pour présenter un bon comportement de réutilisation dans le contexte de caches plus grands. Notre troisième travail est axé sur l'interférence entre les demandes à la demande et les demandes de prélecture à l'accès partagé à la mémoire morte. Ce travail est basé sur deux observations fondamentales de la fraction des requêtes de prélecture générées et de sa corrélation avec l'utilité de prélecture et l'interférence causée par le prélecteur. Au total, deux observations conduisent à contrôler le flux de requêtes de prélecture entre les mémoires LLC et off-chip<br>In this thesis, we have focused on addressing interference at the shared memory-hierarchy resources: last level cache and off-chip memory access in the context of large-scale multicore systems. Towards this end, the first work focused on shared last level caches, where the number of applications sharing the cache could exceed the associativity of the cache. To manage caches in such situations, our solution estimates the cache footprint of applications to approximate how well they could utilize the cache. Quantitative estimate of cache utility explicitly allows enforcing different priorities across applications. The second part brings in prefetch awareness in cache management. In particular, we observe prefetched cache blocks to exhibit good reuse behavior in the context of larger caches. Our third work focuses on addressing interference between on-demand and prefetch requests at the shared off-chip memory access. This work is based on two fundamental observations of the fraction of prefetch requests generated and its correlation with prefetch usefulness and prefetcher-caused interference. Altogether, two observations lead to control the flow of prefetch requests between LLC and off-chip memory
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Costello, Robert. "Adaptive intelligent personalised learning (AIPL) environment." Thesis, University of Hull, 2012. http://hydra.hull.ac.uk/resources/hull:6251.
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As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal. In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis.
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Munde, Gurubachan. "Adaptive iterative learning control." Thesis, University of Exeter, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390139.
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Boniforti, Aldo. "Adaptive Scheduling in Intelligent Transportation Systems." Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99005.
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Intelligent Transportation Systems (ITS) can substantially improve roadsafety and trac eciency. This is possible by allowing communicationamong nearby vehicles and among vehicles and xed roadside units. A popularstandard for vehicular communications is IEEE 802.11p. It is basedon a CSMA/CA MAC method that does not guarantee channel access in anite time and so is not suitable for real-time communications. It also needsmethods to control and limit the load, since the transmission of periodicinformation among vehicles can saturate the channel. In this thesis, a newreal-time scheduling algorithm suitable for ITS applications is introduced. Itis based on a TDMA MAC method, where the roadside unit has the tasks toestimate the channel conditions and assign fractions of time slot to users. Alinear programming approach is considered to minimize an index of utility ofthe transmissions. Multi-hop communication scenarios among the vehiclesare considered for both uplink and downlink communications. It is shownhow the optimal duration of the fraction of time slot depends on the channelconditions. A higher channel gain corresponds to a higher transmission timewhereas a lower channel gain corresponds to a lower transmission time. Itis concluded that the approach studied in the thesis can guarantee a highutility provided that the complexity of the optimization is reduced as thenumber of involved vehicles increases.
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Buche, Cédric. "Adaptive behaviors for virtual entities in participatory virtual environments." Habilitation à diriger des recherches, Université de Bretagne occidentale - Brest, 2012. http://tel.archives-ouvertes.fr/tel-00672518.
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Le CERV constitue à Brest un pôle d'excellence en réalité virtuelle à vocation européenne. Les systèmes que l'on cherche à modéliser sont de plus en plus complexes, du fait essentiellement de la diversité des composants, de la diversité des structures et de la diversité des interactions mises en jeu. Un système est alors a priori un milieu ouvert (apparition/disparition dynamique de composants) et hétérogène (morphologies et comportements variés). La réalité virtuelle implique pleinement l'utilisateur humain dans la simulation, rejoignant ainsi l'approche de la conception. La majorité des travaux en réalité virtuelle concerne l'immersion sensorimotrice de l'utilisateur humain au sein d'univers. Ces univers virtuels offrent à l'utilisateur la sensation d'être dans l'environnement et lui donne la possibilité d'y agir. Pour être complet, il faut également "qu'il s'y passe quelque chose", et pas seulement en terme de résultat des actions de l'utilisateur. Les entités qui peuplent les univers virtuels doivent donc avoir un comportement autonome. Ceci soulève la question suivante : comment doter une entité d'un comportement autonome dans un environnement virtuel complexe auquel l'homme participe ? Des techniques d'intelligence artificielle symbolique ont déjà été appliquées pour définir ces comportements. Mais ces techniques montrent très vite leurs limites car elles sont principalement basées sur des règles de comportements mises a priori par le concepteur. Or, dans des mondes virtuels complexes (simulation ouverte, hétérogène et participative), plusieurs entités vont avoir des comportements imprédictibles (variabilité comportementale des entités autonomes, libre arbitre de l'utilisateur), créant ainsi des situations toujours nouvelles. Et face à une situation non prévue par le programmeur, les entités auront le plus souvent des comportements inadaptés. C'est pourquoi les méthodologies tirées des systèmes artificiels adaptatifs peuvent contribuer à pallier ces limitations. Le travail que je poursuis porte sur la thématique de l'adaptation de comportements d'entités autonomes en environnement virtuel participatif. Adapter son comportement, c'est effectuer des transformations conduisant à s'adapter à son environnement. Cette adaptation aura pour objectif de rendre le comportement de l'entité virtuelle le plus crédible possible (ressemblant à un comportement humain). Pour cela, nous prenons le parti de considérer que l'entité doit apprendre au fur et à mesure des expériences, elle doit anticiper le comportement des autres entités et les conséquences sur l'environnement, elle doit également exploiter la présence de l'utilisateur humain dans l'univers virtuel pour adapter son comportement. Imaginons un monde virtuel où chaque entité, au même titre qu'un humain, aurait son propre comportement qui évolue automatiquement pendant la simulation. C'est tout l'enjeu des travaux de recherche présentés ici.
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Santamaria, Juan Carlos. "Learning adaptive reactive agents." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/9247.
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Bontcheva, Kalina Lubomirova. "Generating adaptive hypertext." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369961.
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Kochenderfer, Mykel J. "Adaptive modelling and planning for learning intelligent behaviour." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/1408.
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An intelligent agent must be capable of using its past experience to develop an understanding of how its actions affect the world in which it is situated. Given some objective, the agent must be able to effectively use its understanding of the world to produce a plan that is robust to the uncertainty present in the world. This thesis presents a novel computational framework called the Adaptive Modelling and Planning System (AMPS) that aims to meet these requirements for intelligence. The challenge of the agent is to use its experience in the world to generate a model. In problems with large state and action spaces, the agent can generalise from limited experience by grouping together similar states and actions, effectively partitioning the state and action spaces into finite sets of regions. This process is called abstraction. Several different abstraction approaches have been proposed in the literature, but the existing algorithms have many limitations. They generally only increase resolution, require a large amount of data before changing the abstraction, do not generalise over actions, and are computationally expensive. AMPS aims to solve these problems using a new kind of approach. AMPS splits and merges existing regions in its abstraction according to a set of heuristics. The system introduces splits using a mechanism related to supervised learning and is defined in a general way, allowing AMPS to leverage a wide variety of representations. The system merges existing regions when an analysis of the current plan indicates that doing so could be useful. Because several different regions may require revision at any given time, AMPS prioritises revision to best utilise whatever computational resources are available. Changes in the abstraction lead to changes in the model, requiring changes to the plan. AMPS prioritises the planning process, and when the agent has time, it replans in high-priority regions. This thesis demonstrates the flexibility and strength of this approach in learning intelligent behaviour from limited experience.
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Ali, Shaaban Aerospace Civil & Mechanical Engineering Australian Defence Force Academy UNSW. "Intelligent adaptive control for nonlinear applications." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/39185.
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The thesis deals with the design and implementation of an Adaptive Flight Control technique for Unmanned Aerial Vehicles (UAVs). The application of UAVs has been increasing exponentially in the last decade both in Military and Civilian fronts. These UAVs fly at very low speeds and Reynolds numbers, have nonlinear coupling, and tend to exhibit time varying characteristics. In addition, due to the variety of missions, they fly in uncertain environments exposing themselves to unpredictable external disturbances. The successful completion of the UAV missions is largely dependent on the accuracy of the control provided by the flight controllers. Thus there is a necessity for accurate and robust flight controllers. These controllers should be able to adapt to the changes in the dynamics due to internal and external changes. From the available literature, it is known that, one of the better suited adaptive controllers is the model based controller. The design and implementation of model based adaptive controller is discussed in the thesis. A critical issue in the design and application of model based control is the online identification of the UAV dynamics from the available sensors using the onboard processing capability. For this, proper instrumentation in terms of sensors and avionics for two platforms developed at UNSW@ADFA is discussed. Using the flight data from the remotely flown platforms, state space identification and fuzzy identification are developed to mimic the UAV dynamics. Real time validations using Hardware in Loop (HIL) simulations show that both the methods are feasible for control. A finer comparison showed that the accuracy of identification using fuzzy systems is better than the state space technique. The flight tests with real time online identification confirmed the feasibility of fuzzy identification for intelligent control. Hence two adaptive controllers based on the fuzzy identification are developed. The first adaptive controller is a hybrid indirect adaptive controller that utilises the model sensitivity in addition to output error for adaptation. The feedback of the model sensitivity function to adapt the parameters of the controller is shown to have beneficial effects, both in terms of convergence and accuracy. HIL simulations applied to the control of roll stabilised pitch autopilot for a typical UAV demonstrate the improvements compared to the direct adaptive controller. Next a novel fuzzy model based inversion controller is presented. The analytical approximate inversion proposed in this thesis does not increase the computational effort. The comparisons of this controller with other controller for a benchmark problem are presented using numerical simulations. The results bring out the superiority of this technique over other techniques. The extension of the analytical inversion based controller for multiple input multiple output problem is presented for the design of roll stabilised pitch autopilot for a UAV. The results of the HIL simulations are discussed for a typical UAV. Finally, flight test results for angle of attack control of one of the UAV platforms at UNSW@ADFA are presented. The flight test results show that the adaptive controller is capable of controlling the UAV suitably in a real environment, demonstrating its robustness characteristics.
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Kang, Hoon. "Intelligent/adaptive control strategies for robot manipulators." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/13882.
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Park, Kyounga. "Learning user preferences for intelligent adaptive in-vehicle navigation." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506034.
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Holmes, Michael Henry. "Comprehension based adaptive learning systems." Thesis, Manchester Metropolitan University, 2017. http://e-space.mmu.ac.uk/621011/.
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Conversational Intelligent Tutoring Systems aim to mimic the adaptive behaviour of human tutors by delivering tutorial content as part of a dynamic exchange of information conducted using natural language. Deciding when it is beneficial to intervene in a student’s learning process is an important skill for tutoring. Human tutors use prior knowledge about the student, discourse content and learner non-verbal behaviour to choose when intervention will help learners overcome impasse. Experienced human tutors adapt discourse and pedagogy based on recognition of comprehension and non-comprehension indicative learner behaviour. In this research non-verbal behaviour is explored as a method of computationally analysing reading comprehension so as to equip an intelligent conversational agent with the human-like ability to estimate comprehension from non-verbal behaviour as a decision making trigger for feedback, prompts or hints. This thesis presents research that combines a conversational intelligent tutoring system (CITS) with near real-time comprehension classification based on modelling of e-learner non-verbal behaviour to estimate learner comprehension during on-screen conversational tutoring and to use comprehension classifications as a trigger for intervening with hints, prompts or feedback for the learner. To improve the effectiveness of tuition in e-learning, this research aims to design, develop and demonstrate novel computational methods for modelling e-learner comprehension of on-screen information in near real-time and for adapting CITS tutorial discourse and pedagogy in response to perception of comprehension indicative behaviour. The contribution of this research is to detail the motivation for, design of, and evaluation of a system which has the human-like ability to introduce micro-adaptive feedback into tutorial discourse in response to automatic perception of e-learner reading comprehension. This research evaluates empirically whether e-learner non-verbal behaviour can be modelled to classify comprehension in near real-time and presents a near real-time comprehension classification system which achieves normalised comprehension classification accuracy of 75%. Understanding e-learner comprehension creates exciting opportunities for advanced personalisation of materials, discourse, challenge and the digital environment itself. The research suggests a benefit is gained from comprehension based adaptation in conversational intelligent tutoring systems, with a controlled trial of a comprehension based adaptive CITS called Hendrix 2.0 showing increases in tutorial assessment scores of up to 17% when comprehension based discourse adaptation is deployed to scaffold the learning experience.
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LIAO, XIAOQUN (SHERRY). "CREATIVE LEARNING FOR INTELLIGENT ROBOTS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141140265.
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Jung, Ju-Hwan. "Intelligent systems for strategic power infrastructure defense /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/5971.
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Martin, Cheryl Elizabeth Duty. "Adaptive decision-making frameworks for multi-agent systems." Access restricted to users with UT Austin EID, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3023557.
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Xu, Peng. "A Learning based Adaptive Cruise and Lane Control System." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1530242159705226.
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Xu, Andong. "Flexible adaptive-network-based fuzzy inference system." Diss., Online access via UMI:, 2006.
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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2006.<br>Includes bibliographical references.
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Lockhart, Tony F. "Increasing motivation by adapting intelligent tutoring instruction to learner achievement goals." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39556.
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The impact of affect on learning and performance has caused many researchers in the field of cognitive psychology to acknowledge the value of motivationally supportive instruction. Goal orientation, which refers to the perceptions and behaviors of the learner in achievement situations, has been the most predominant theory in learning motivation. However, research suggests multiple components are responsible for affecting student cognitive engagement. The traditional framework distinguishes individuals who are self-motivated to master challenging tasks from those who are motivated to earn favorable judgments of performance as intrinsic and extrinsic learners, respectively. In addition, learners may be further categorized by an eagerness to ensure a positive outcome or by their vigilance in avoiding negative outcomes. As such, my research explores how these motivational categories can be utilized to construct a more robust instructional model.
The objective of this research is to evaluate the effectiveness of adaptive remediation strategies on motivation and learning performance. Research suggests the cost of integrating cognitive tasks with error analysis outweigh the benefits of sparse learning gains. However, further investigation is required to understand how feedback can improve these outcomes. The experiment presented here seeks to evaluate the adaptive instruction of two pedagogical agents embedded within two separate versions of the Virtual BNI Trainer. The basic coach uses a model of the learner's experience level to determine an appropriate level of elaboration required during remediation. In contrast, the motivationally enhanced coach uses a model of the learner's goal orientation to construct feedback that appeals to their natural disposition.
A controlled experiment was conducted to evaluate the effects of adaptive instruction on student self-efficacy, engagement, and learning performance in the Virtual BNI Training Environment. The results of this experiment are used to establish guidelines for integrating goal orientation, error analysis, and feedback within a virtual coach, to improve motivation and learning performance. In addition, these findings also indicate areas for future research.
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Nichols, David. "Intelligent student systems : an application of viewpoints to intelligent learning environments." Thesis, Lancaster University, 1993. http://eprints.lancs.ac.uk/53447/.
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Gon?alves, Andr? Vin?cius. "Modelagem autom?tica e din?mica de estilos de aprendizagem em sistemas adaptativos e inteligentes para educa??o a dist?ncia: estudo comparativo entre duas abordagens." UFVJM, 2015. http://acervo.ufvjm.edu.br/jspui/handle/1/1154.
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Previous issue date: 2016-06<br>Nos ?ltimos dez anos muitos pesquisadores t?m realizado estudos sobre assist?ncia
personalizada e inteligente em Ambientes Educacionais a Dist?ncia, baseada na identifica??o
dos Estilos de Aprendizagem. Sabe-se que o aprendizado ? algo extremamente
particular, pois cada estudante possui estilos pr?prios e pode sofrer mudan?as diante
de situa??es diversas como, por exemplo, objetivo, motiva??o, personalidade, etc. Por
isso, o conceito de adaptabilidade do conte?do did?tico tem se tornado de grande
import?ncia na personaliza??o do Sistema de Gerenciamento de Aprendizagem (SGA).
Diante desse fato, Dor?a (2012) prop?e uma abordagem de Sistema Adaptativo e
Inteligente para Educa??o (SAIE), utilizando t?cnicas probabil?sticas e Intelig?ncia
Artificial (IA), capaz de detectar e adaptar, de maneira din?mica e autom?tica, os estilos
de aprendizagem do estudante, considerando o Modelo de Estilo de Aprendizagem
Felder-Silverman?s. Ap?s pesquisa detalhada, foram propostas algumas adapta??es
baseadas na abordagem original, alterando o funcionamento de dois componentes
espec?ficos: o M?dulo Pedag?gico e o Componente de Modelagem do Estudante. Al?m
disso, prop?e-se uma nova estrutura do Modelo Estudante, contemplando o hist?rico
de desempenho do aluno nos processos avaliativos. Por conseguinte, realizaram-se
testes para avaliar os impactos de tais mudan?as por meio uma compara??o estat?stica
utilizando o m?todo T-Pareado. Pelos resultados obtidos, as ideias deste trabalho proporcionaram
uma melhora m?dia de 6,07% no desempenho avaliativo do estudante
e uma redu??o m?dia de 68,27% nos problemas de aprendizagem, demonstrando
efici?ncia e efic?cia da proposta.<br>Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2015.<br>Since last decade many researchers have been conducting studies on personalized
and intelligent assistance in distance education based on identification of learning
styles. It is known that learning is something very particular because each student
has their own styles and are subject to change on a variety of situations such as goal,
motivation, personality, etc. Therefore, this study discusses the concept of adaptability
of educational content as a way to provide customization of Learning Management
System (LMS). Through probabilistic techniques and Artificial Intelligence (AI), Dor?a
(2012) proposed a approach Adaptive and Intelligent System for Education (AIES) able
to dynamically and automatically detect, select and adapt learning objects based on
the student?s profile through Felder-Silverman Learning Styles Model (FSLSM). After
detailed study, it has been proposed some adaptations based on this approach, thereby
altering the operation of two specific components: the Pedagogical Module and the
Student Modeling Component. In addition, it is proposed a new structure Model Student,
considering learner performance history in the evaluation processes. Therefore, it
carried out tests to assess the impacts of such changes through a statistical comparison
by T-Paired method. From the results, the ideas in this work provides an average
improvement of 6.07% in the performance evaluation of the student and an average
reduction of 68.27% in the learning problems, demonstrating proposal of efficiency and
effectiveness.
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Okpo, Juliet Airenvbiegbe. "Adaptive exercise selection for an intelligent tutoring system." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238127.
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Adapting to learner characteristics is essential when selecting exercises for learners in an intelligent tutoring system. This thesis investigates how humans adapt next exercise selection (in particular difficulty level) to learner personality (self-esteem), invested mental effort, and performance to inspire an adaptive exercise selection algorithm. First, we describe the investigations to produce validated materials for the main studies, namely the creation and validation of self-esteem personality stories, mental effort statements, and mathematical exercises with varying levels of difficulty. Next, through empirical studies, we investigate the impact on exercise selection of learner's selfesteem (low versus high self-esteem) and effort (minimal, little, moderate, much, and all possible effort). Three studies investigate this for learners who had different performances on a previous exercise: just passing, just failing, and performed well. Participants considered a fictional learner with a certain performance, self-esteem and effort, and selected the difficulty level of the next mathematical exercise. We found that self-esteem, mental effort, and performance all impacted the difficulty level of the exercises selected for learners. Using the results from the studies, we generated an algorithm that selects exercises with varying difficulty levels adapted to learner characteristics. Finally, through a survey with professional teachers, we evaluated our algorithm and found that the algorithm's adaptations were appropriate in general.
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Sime, Julie-Ann. "Model switching in intelligent training systems." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1396.
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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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