Dissertations / Theses on the topic 'Complex Systems Learning Environments'

e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres are limited. Existing technologies such as grid, web services and agents are promising better results. In this research a new architecture has been developed and adopted to make the e-learning environment more dynamic and scalable by dividing it into regional data grids which are managed and monitored by agents. Multi-agent technology has been applied to integrate each regional data grid with others in order to produce an architecture which is more scalable, reliable, and efficient. The result we refer to as Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE). Our RDADeLE architecture is an agent-based grid environment which is composed of components such as learners, staff, nodes, regional grids, grid services and Learning Objects (LOs). These components are built and organised as a multi-agent system (MAS) using the Java Agent Development (JADE) platform. The main role of the agents in our architecture is to control and monitor grid components in order to build an adaptable, extensible, and flexible grid-based e-learning system. Two techniques have been developed and adopted in the architecture to build LOs' information and grid services. The first technique is the XML-based Registries Technique (XRT). In this technique LOs' information is built using XML registries to be discovered by the learners. The registries are written in Dublin Core Metadata Initiative (DCMI) format. The second technique is the Registered-based Services Technique (RST). In this technique the services are grid services which are built using agents. The services are registered with the Directory Facilitator (DF) of a JADE platform in order to be discovered by all other components. All components of the RDADeLE system, including grid service, are built as a multi-agent system (MAS). Each regional grid in the first technique has only its own registry, whereas in the second technique the grid services of all regional grids have to be registered with the DF. We have evaluated the RDADeLE system guided by both techniques by building a simulation of the prototype. The prototype has a main interface which consists of the name of the system (RDADeLE) and a specification table which includes Number of Regional Grids, Number of Nodes, Maximum Number of Learners connected to each node, and Number of Grid Services to be filled by the administrator of the RDADeLE system in order to create the prototype. Using the RST technique shows that the RDADeLE system can be built with more regional grids with less memory consumption. Moreover, using the RST technique shows that more grid services can be registered in the RDADeLE system with a lower average search time and the search performance is increased compared with the XRT technique. Finally, using one or both techniques, the XRT or the RST, in the prototype does not affect the reliability of the RDADeLE system.

This thesis explores system capacity building, in particular, the purpose of system capacity building and how leaders, in the context of Leading for Learning Project, enabled whole of system capacity building with a focus on sustained engagement with moral purpose. It is argued, however, that the purpose and scope of system capacity building is often conceptually limited because it is understood within the current regulatory and performance focused education reform environment. This thesis, therefore, offers an alternative perspective by engaging with the theoretical underpinnings of complexity theory. As such, this thesis offers a conceptualisation of education systems as complex adaptive systems and system capacity building as a complex and emergent process. The thesis presents a radical reframing of education systems arguing that education systems are better understood as open, dynamic, and emergent systems, constituted of many interdependent relationships throughout the system.

The overarching aim of this programme of work was to evaluate the effectiveness of the existing learning environment within the Australian Institute of Sport (AIS) elite springboard diving programme. Unique to the current research programme, is the application of ideas from an established theory of motor learning, specifically ecological dynamics, to an applied high performance training environment. In this research programme springboard diving is examined as a complex system, where individual, task, and environmental constraints are continually interacting to shape performance. As a consequence, this thesis presents some necessary and unique insights into representative learning design and movement adaptations in a sample of elite athletes. The questions examined in this programme of work relate to how best to structure practice, which is central to developing an effective learning environment in a high performance setting. Specifically, the series of studies reported in the chapters of this doctoral thesis: (i) provide evidence for the importance of designing representative practice tasks in training; (ii) establish that completed and baulked (prematurely terminated) take-offs are not different enough to justify the abortion of a planned dive; and (iii), confirm that elite athletes performing complex skills are able to adapt their movement patterns to achieve consistent performance outcomes from variable dive take-off conditions. Chapters One and Two of the thesis provide an overview of the theoretical ideas framing the programme of work, and include a review of literature pertinent to the research aims and subsequent empirical chapters. Chapter Three examined the representativeness of take-off tasks completed in the two AIS diving training facilities routinely used in springboard diving. Results highlighted differences in the preparatory phase of reverse dive take-offs completed by elite divers during normal training tasks in the dry-land and aquatic training environments. The most noticeable differences in dive take-off between environments began during the hurdle (step, jump, height and flight) where the diver generates the necessary momentum to complete the dive. Consequently, greater step lengths, jump heights and flight times, resulted in greater board depression prior to take-off in the aquatic environment where the dives required greater amounts of rotation. The differences observed between the preparatory phases of reverse dive take-offs completed in the dry-land and aquatic training environments are arguably a consequence of the constraints of the training environment. Specifically, differences in the environmental information available to the athletes, and the need to alter the landing (feet first vs. wrist first landing) from the take-off, resulted in a decoupling of important perception and action information and a decomposition of the dive take-off task. In attempting to only practise high quality dives, many athletes have followed a traditional motor learning approach (Schmidt, 1975) and tried to eliminate take-off variations during training. Chapter Four examined whether observable differences existed between the movement kinematics of elite divers in the preparation phases of baulked (prematurely terminated) and completed take-offs that might justify this approach to training. Qualitative and quantitative analyses of variability within conditions revealed greater consistency and less variability when dives were completed, and greater variability amongst baulked take-offs for all participants. Based on these findings, it is probable that athletes choose to abort a planned take-off when they detect small variations from the movement patterns (e.g., step lengths, jump height, springboard depression) of highly practiced comfortable dives. However, with no major differences in coordination patterns (topology of the angle-angle plots), and the potential for negative performance outcomes in competition, there appears to be no training advantage in baulking on unsatisfactory take-offs during training, except when a threat of injury is perceived by the athlete. Instead, it was considered that enhancing the athletes' movement adaptability would be a more functional motor learning strategy. In Chapter Five, a twelve-week training programme was conducted to determine whether a sample of elite divers were able to adapt their movement patterns and complete dives successfully, regardless of the perceived quality of their preparatory movements on the springboard. The data indeed suggested that elite divers were able to adapt their movements during the preparatory phase of the take-off and complete good quality dives under more varied take-off conditions; displaying greater consistency and stability in the key performance outcome (dive entry). These findings are in line with previous research findings from other sports (e.g., shooting, triple jump and basketball) and demonstrate how functional or compensatory movement variability can afford greater flexibility in task execution. By previously only practising dives with good quality take-offs, it can be argued that divers only developed strong couplings between information and movement under very specific performance circumstances. As a result, this sample was sometimes characterised by poor performance in competition when the athletes experienced a suboptimal take-off. Throughout this training programme, where divers were encouraged to minimise baulking and attempt to complete every dive, they demonstrated that it was possible to strengthen the information and movement coupling in a variety of performance circumstances, widening of the basin of performance solutions and providing alternative couplings to solve a performance problem even when the take-off was not ideal. The results of this programme of research provide theoretical and experimental implications for understanding representative learning design and movement pattern variability in applied sports science research. Theoretically, this PhD programme contributes empirical evidence to demonstrate the importance of representative design in the training environments of high performance sports programmes. Specifically, this thesis advocates for the design of learning environments that effectively capture and enhance functional and flexible movement responses representative of performance contexts. Further, data from this thesis showed that elite athletes performing complex tasks were able to adapt their movements in the preparatory phase and complete good quality dives under more varied take-off conditions. This finding signals some significant practical implications for athletes, coaches and sports scientists. As such, it is recommended that care should be taken by coaches when designing practice tasks since the clear implication is that athletes need to practice adapting movement patterns during ongoing regulation of multi-articular coordination tasks. For example, volleyball servers can adapt to small variations in the ball toss phase, long jumpers can visually regulate gait as they prepare for the take-off, and springboard divers need to continue to practice adapting their take-off from the hurdle step. In summary, the studies of this programme of work have confirmed that the task constraints of training environments in elite sport performance programmes need to provide a faithful simulation of a competitive performance environment in order that performance outcomes may be stabilised with practice. Further, it is apparent that training environments can be enhanced by ensuring the representative design of task constraints, which have high action fidelity with the performance context. Ultimately, this study recommends that the traditional coaching adage 'perfect practice makes perfect", be reconsidered; instead advocating that practice should be, as Bernstein (1967) suggested, "repetition without repetition".

Muitas instituições de ensino superior (IES) buscam novas formas de aperfeiçoarem o processo ensino-aprendizagem visando adequarem as aulas tradicionais às tecnologias emergentes advindas da Web 2.0, como as mídias sociais. Nesse processo de reinvenção, o docente deve capacitar-se a ministrar aulas não apenas com o intuito de disseminar conhecimentos tácitos e explícitos, mas também de liderar os estudantes na sala de aula presencial e nos ambientes virtuais de aprendizagem, conduzindo-os a superarem suas expectativas quanto ao aprendizado. Analisando perfis de liderança existentes, a presente dissertação, focada em um Estudo de Caso, busca identificar qual o perfil ideal de liderança do docente em ambientes híbridos, utilizando-se para tal do Índice de Satisfação de Grupo, análise de gráficos e do discurso dos estudantes. O método de ensino adotado pelo docente foi o PBL (Problem Based Learning), dando-se ênfase ao desenvolvimento do pensamento critico. A pesquisa realizou-se junto a disciplinas do Departamento de Engenharia de Produção da EESC-USP, onde estudantes de graduação e pós-graduação foram desafiados pelo método PBL a pensarem de forma crítica na construção do próprio conhecimento. Os resultados obtidos por meio de questionários aplicados ao final dos semestres de 2011 e 2012 mostram que a liderança Laissez-faire não é indicada na fase de implantação do PBL, necessitando maior interferência do docente para a quebra do paradigma entre o modelo de ensino tradicional e o novo. Conclui-se que a conexão entre o processo ensino-aprendizagem tradicional e o novo possa ser feita por meio da liderança situacional, alternando a liderança transformacional e a transacional de acordo com o contexto, excluindo-se a Laissez-faire.
Many institutions have developed new ways to improve the teaching-learning process, trying to adequate the classes to the emerging new technologies based on Web 2.0, as in the case of social media. In this re-inventing process teachers must have the leadership skills both in classrooms and virtual learning environments, allowing the students to overcome their expectations of the learning process. This dissertation is focused on a case study that seeks to identify what is the ideal leadership profile into hybrid environments, using a group satisfaction index, graphical analysis and student speeching skills. The teaching methodology used by the teacher was the PBL (Problem-Based Learning) that emphasizes the critical mindset. The research was carried out during the disciplines into the Manufacturing Engineering graduation and post-graduation program of Universidade de São Paulo (USP) EESC department. The students were challenged by the PBL methodology to think about the best way to build their own know how. The data obtained with the application of questionaries in the 2011 and 2012 semesters showed that the \"Laissez-Faire\" leadership is not recommended during the PBL methodology implementation, requiring more interferences and attention from the teacher to break the paradigm of the traditional teaching models. The conclusion is that the connection between the traditional and the new teaching-learning processes can be built using situational leadership, alternating between transformational leadership and transactional models, contextually, excluding the Laissez-faire.

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UNISINOS - Universidade do Vale do Rio dos Sinos
O presente estudo concebe o papel central da língua no desenvolvimento humano por meio de um agir linguageiro. Nessa perspectiva, compreende-se que a linguagem se situa em práticas interacionais e está em permanente transformação. Este estudo tem caráter empírico de cunho qualitativo, inserido na plataforma Moodle, investigando a especificidade das interações realizadas em Chats, em uma disciplina de Ensino à Distância (EAD) na perspectiva da Teoria da Complexidade (GLEICK, 1994; JOHNSON 2003; LARSENFREEMAN 1997, 2008, 2009; MORIN 2008) e do Interacionismo Sociodiscursivo - ISD (BRONCKART, 1999, 2008). A compreensão epistemológica deste estudo abarca o agir humano em desenvolvimento caracterizado como um sistema adaptativo complexo. Sendo que, a partir das práticas de nível mais baixo, mas não menos complexas, os comportamentos acabam por emergir. Assim, os interagentes desenvolvem comportamentos observáveis que emergem das condições iniciais, encaminhando-se às manifestações de auto-organização. O agir humano situado na plataforma, assim como as práticas situadas por meio de tipos específicos de discursos (BRONCKART, 1999) estão entendidos como geradores de movimentos de complexidade. As relações discursivas tecnologicamente situadas foram observadas quanto à forma como os interagentes se adaptavam ao contexto discursivo. A partir das interações analisadas, elencaram-se, então, tipos de discurso e foram propostos quatro tipos de Movimentos de Complexidade que emergiram das interações discursivas: Movimento 1 – Dinamicidade discursiva entre interagentes; Movimento 2 – Não-linearidade interacional; Movimento 3 – Adaptação de agentividade (espaço-tempo) e Movimento 4 – Comportamento emergente. A análise do agir linguageiro, situado por meio de tipos de discurso identificados, apresentou baixo índice de variação da escrita em linguagem formal; como era de se esperar, dado o caráter formal da troca entre pares. O estudo permitiu evidenciar que os tipos de discurso se adaptam na medida em que os interagentes passam por mudanças interacionais de fases co-construídas em processo, alternando entre os tipos de discurso relato interativo e relato misto interativo-teórico pela ausência do discurso teórico.
This study conceives the central role of language in the human development through language acting. In this perspective, it is comprehended that language is situated in interactional practices and it is permanently transformed. This study has an empirical character and a qualitative approach, inserted in the Moodle platform, investigating the specificity of the interactions made in Chats in a Distance Learning Environment according to the Complexity Theory perspective (GLEICK, 1989; JOHNSON 2003; LARSEN-FREEMAN 1997, 2008, 2009; MORIN 2008) and the Sociodiscursive Interactionism perspective (BRONCKART, 1999, 2008). The epistemological comprehension of this study considers that the language acting under development is characterized as a complex adaptive system, in which behaviors eventually emerge from practices of lower level, but not less complex. Then, the interactants develop observing behaviors that emerge from the initial conditions, heading towards self-organization manifestations. The situated language acting on the platform, as well as the situated practices through specific types of discourse (BRONCKART, 2008), are understood as generators of complexity movements. The ways interactants adapted themselves to the discursive context were observed as discursive relations technologically situated. From the analyzed interactions, types of discourse were established and four types of complexity movements, which emerged from the discursive interactions, were proposed: Movement 1 – Discursive Dynamics among interactants; Movement 2 – Non-Interactional linearity; Movement 3 – Agentivity Adaptation (space-time); Movement 4 – Emergent Behavior. The analysis of the situated language acting, through identified types of discourse, presented a low rate of variation in formal language use, as it was expected, due to the formal character of the exchange among pairs. The study allowed us to evidence that the types of discourse are adaptable as the interactants go through phases of interactional change coconstructed in process, alternating between interactive-reporting and mixed theoreticalinteractive types of discourse, by the absence of theoretical discourse.

In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the impact of increasing the complexity of the training environment over time. This was compared to using a fixed complexity. Also, we investigated the impact of using a pre-trained agent as a starting point for training in an environment with a different complexity, compared to an untrained agent. The scope was limited to only training and analyzing agents playing a variant of the 2D game Snake. Random obstacles were placed on the map, and complexity corresponds to the amount of obstacles. Performance was measured in terms of eaten fruits. The results showed benefits in overall performance for the agent trained in increasingly complex environments. With regard to previous research, it was concluded that this seems to hold generally, but more research is needed on the topic. Also, the results displayed benefits of using a pre-trained model as a starting point for training in a different complexity environment, which was hypothesized.
I denna studie använde vi deep reinforcement learning för att träna autonoma agenter och utvärderade inverkan av att använda miljöer med ökande komplexitet över tid. Detta jämfördes med att använda en fixerad komplexitet. Utöver detta jämförde vi att använda en tränad agent som startpunkt för träning i en miljö med en annan komplexitet, jämfört med att använda en otränad agent. Studien avgränsades till att bara träna och analysera agenter på en variant av 2D-spelet Snake. Hinder placerades slumpmässigt ut på kartan, och komplexiteten motsvarar antalet hinder. Prestationen mättes i antal frukter som agenten lyckades äta. Resultaten visade att agenten som tränades i miljöer med ökande komplexitet presterade bättre totalt sett. Med hänsyn till tidigare forskning drogs slutsatsen att detta verkar vara ett generellt fenomen, men att mer forskning behövs på ämnet. Vidare visade resultaten att det finns fördelar med att använda en redan tränad agent som startpunkt för träning i en miljö med en annan komplexitet, vilket var en del av författarnas hypotes.

Two observations underpin this thesis; 1. There is a need for automated pattem-recognition techniques that allow processes requiring skills normally associated with the human brain to be carried out rapidly, reliably and cheaply, and; 2. The current methods applied to solving artificial intelligence (AI) problems are insufficient to the task of creating generalised systems capable of pattem-recognition and environmental interaction. Neural networks (NNs) are a good method of solving AI problems that are difficult or impossible to solve using knowledge-based or symbolic techniques. NNs provide the flexibility to analyse poorly-defined systems or systems that are general in nature, and also provide the ability to learn from noisy, complex data sets. The main problem with the use of NNs to date has been that one NN's structure and dynamics may work for a specific problem, but if this problem is changed slightly then it is difficult to determine the optimal settings for the network to enable it to adapt to the new situation. The use of evolutionary methods is emphasised throughout this thesis as a way of optimising NN system performance. Several methods have been developed through the course of this thesis that improve the performance of NN models. One of the most important is the use of a biologically plausible node and connection modification algorithm. In this method, local effects such as the activation levels of nodes at either end of a connection or a node's past activation history are the only input parameters which network components use for their adjustment. Included in the biological plausibility argument are NN structuring methods that mimic specific areas of the brain. One example is the visual system, in which a pyramidal structure is applied that permits a hierarchical pattern recognition process to develop. This process builds the image recognition up from small 'substructures' in successive layers, allowing the system to recognise objects that are not specifically defined by the user. Arguments are made that an AI systems's utility is limited if it does not have the capability of interacting with its environment. A system that merely observes without attempting to alter or exist within an environment is only half of the story. From a biological standpoint, intelligence is the result of successive generations of organisms interacting with and altering their environment. Limiting an AI system's ability to interact with the environment can only place restrictions on the capabilities of that system, not improve them. Following development of a suite of applicable pattem-recognition techniques, work is carried out in order to implement these methods within a simple environment. For the moment, a virtual 'block world' is used that is relatively easy and cheap to manipulate. The importance of both modularity and sensory feedback to the ability to develop complex behaviours is investigated, with these two concepts included in the overall evolutionary strategy of system development. The results obtained show that the techniques developed provide a pattem- recognition and learning system that is capable of being applied to general problems and that learns without human intervention. In comparison to classical NN techniques the systems developed show superior learning abilities and can be applied in less specific situations. The use of modularity and sensory feedback in the animat simulations has allowed the development of behavioural patterns that are difficult to achieve using homogeneous, input-output systems. Evolutionary methods have allowed system optimisation in a way that is impossible to achieve through trial and error, and which also permit the system to be easily fine-tuned towards specific problems and situations. With current advances in computer speed and memory capacity, it is now possible to implement NNs comparable in size to the nervous systems of small animals. The methods used here provide the potential to provide these NNs with the sophistication displayed by their organic counterparts.

This thesis contends that the growing phenomena of multi-user networked "learning environments" should be treated as distributed interactive systems and that their developers should be aware of the systems and networks issues involved in their construction and maintenance. Such environments are henceforth referred to as distributed learning environments, or DLEs. Three major themes are identified as part of systems support: i) shared resource coherence in DLEs; ii) Quality of Service for the end- users of DLEs; and iii) the need for an integrating framework to develop, deploy and manage DLEs. The thesis reports on several distinct implementations and investigations that are each linked by one or more of those themes. Initially, responsiveness and coherence emerged as potentially conflicting requirements, and although a system was built that successfully resolved this conflict it proved difficult to move from the "clean room" conditions of a research project into a real world learning context. Accordingly, subsequent systems adopted a web-based approach to aid deployment in realistic settings. Indeed, production versions of these systems have been used extensively in credit-bearing modules in several Scottish Universities. Interactive responsiveness then emerged as a major Quality of Service issue in its own right, and motivated a series of investigations into the sources of delay, as experienced by end users of web-oriented distributed learning environments. Investigations into this issue provided insight into the nature of web-oriented interactive distributed learning and highlighted the need to be QoS-aware. As the volume and the range of usage of distributed learning applications increased the need for an integrating framework emerged. This required identifying and supporting a wide variety of educational resource types and also the key roles occupied by users of the system, such as tutors, students, supervisors, service providers, administrators, examiners. The thesis reports on the approaches taken and lessons learned from researching, designing and implementing systems which support distributed learning. As such, it constitutes a documented body of work that can inform the future design and deployment of distributed learning environments.

Orientador: Marcus Vinicius Maltempi
Resumo: A internet tem sido uma fonte crescente de recursos que podem ser utilizados para o ensino. Pesquisas têm mostrado a contribuição de softwares, vídeos, dentre outros materiais online para o ensino e aprendizagem da Matemática, entretanto é comum perceber que as potencialidades da internet têm sido pouco aproveitadas nas aulas de Matemática. Nesse sentido, a sala de aula caracterizada por sua complexidade, mesmo quando faz uso de recursos da internet mantém práticas de ensino e aprendizagem que expressam culturas socialmente produzidas quando a internet ainda não se fazia presente. Nessa direção, contei com os pressupostos da investigação qualitativa e de um trabalho colaborativo entre mim e as professoras de duas turmas do primeiro ano de escolas públicas estaduais do estado de São Paulo, sendo uma na cidade de Vinhedo e outra na cidade de Nova Odessa: Escola Técnica Estadual (ETEC), para buscar compreender práticas culturais de ensinar e aprender (re)constituídas nas aulas de Matemática mediadas pela internet em um ambiente híbrido. Considerei as características do ambiente híbrido para a condução das aulas cujo estudo sobre o conceito de Função foi o foco em ambas as escolas. Adotei as análises narrativas para expressar a produção do meu conhecimento sobre os dados constituídos nas duas salas de aulas, as quais são vistas nessa pesquisa como comunidades de prática que são sistemas complexos. Assim, meu olhar para os dados foi a partir da Ciência da Complexidade. Os resultad... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The internet is currently offering more and more resources that can be used for teaching purposes. Research has shown how the use of software, video, and other online materials are contributing to the teaching and learning of Mathematics. However, it is noticeable that the potentialities of the Internet have been poorly used in Mathematics classes. In this sense, even when the classroom – characterized by its complexity – uses internet resources, teaching and learning practices will mostly express a culture socially produced when the Internet was not present. Therefore, this research seeks to understand cultural practices (re)constituted in mathematics classes mediated by the Internet in a hybrid environment. The research is based on qualitative investigation, and on collaborative work between myself and two grade 10 teachers from public schools in the state of São Paulo. One school was a regular school in the city of Vinhedo, and the other was a technical school in the city of Nova Odessa. In both schools, classes were focused on the concept of function, and the features of a hybrid environment were considered during class implementation. Narrative analysis was used to communicate the knowledge built from the data produced in the two classrooms, which are acknowledged in this research as complex systems. As such, the data was analyzed from the lens of Complexity Science. Research outcomes show that teaching is facilitated by the potentialities of a hybrid environment in orde... (Complete abstract click electronic access below)
Doutor

Thesis (M.S. in Modeling, Virtual Environments, and Simulation (MOVES))--Naval Postgraduate School, June 2007.
Thesis Advisor(s): Christian Darken. "June 2007." Includes bibliographical references (p. 47). Also available in print.

In this dissertation, we developed predictive models for legged and limbless locomotion on dry, homogeneous granular media. The vertical plane Resistive Force Theory (RFT) for frictional granular fluids accurately predicted the reaction forces on intruders (with complex geometries) translating and rotating at low speeds ( < 0.5 m/s). Using RFT and multibody simulation, we predicted the forward moving speed of legged robots. During the locomotion of lightweight robots and animals where instantaneous limb penetration speed can reach values greater than ~0.5 m/s, a Discrete Element Method (DEM) simulation was developed to capture the limb-ground interaction. We demonstrated that hydrodynamic-like forces generated by accelerated particles can balance the robot weight and inertia, and promote the rapid movement on granular media. Forces from the environment can not only determine locomotion dynamics, but also affect the locomotion strategy. We studied and simulated the limbless locomotion of snakes in a heterogeneous environment and demonstrated how touch sensing was used to adjust the movement pattern. In heterogeneous environments, the long-term locomotion dynamics is also poorly understood. We presented a theory for transport and diffusion in such settings.

Thesis advisor: Patrick J. McQuillan
In the 2007-2008 school year, the author conducted a collaborative case study (Stake, 2000) with the goal of discovering and describing "emergent learning" in three high school classrooms. Emergent learning, defined as the acquisition of new knowledge by an entire group when no individual member of the group possessed it before, is implied by the work of many theorists working on an educational analog of a natural phenomenon called a complex adaptive system. Complex adaptive systems are well networked collectives of agents that are non-linear, bounded and synergistic. The author theorized that classes that maximized the features of complex adaptive systems could produce emergent learning (a form of synergy), and that there was a continuum of this complexity, producing a related continuum of emergence. After observing a co-curricular jazz group, an English class, and a geometry class for most of one academic year, collecting artifacts and interviewing three students and a teacher from each class, the author determined that there was indeed a continuum of complexity. He found that the actively complex nature of the Jazz Rock Ensemble produced an environment where emergence was the norm, with the ensemble producing works of music, new to the world, with each performance. The English section harnessed the chaotic tendencies of students to optimize cognitive dissonance and frequently produce emergent learning, while the mathematics section approached the learning process in a way that was too rigidly linear to allow detectable emergence to occur
Thesis (PhD) — Boston College, 2009
Submitted to: Boston College. Lynch School of Education
Discipline: Teacher Education, Special Education, Curriculum and Instruction

Audience Response Systems (ARSs) provide a promising opportunity to address issues occurring in traditional higher education, e.g., the lack of interaction, by allowing students to participate anonymously in lectures using their mobile devices. This can promote the students' attention, increase the interaction between the lecturer and the students and foster active thinking during class. In order to choose an appropriate ARS, numerous surveys list and classify these systems according to different criteria, e.g., supported features and platforms. [From the introduction]

Doctor of Philosophy (PhD)
Environmental issues are complex and understanding them involves integration of different areas of knowledge, feedback and time delays, however strategies to cope with complexity are not often used or taught in environmental education. The aim of this thesis is to examine the benefit of three such strategies for environmental education: multiple external representations, learning from models, and collaborative learning. The socio-environmental system modelled was visitor impact in a national park in Australia. Students in Year 9 and 10 from two schools were given a text description (Text group) and either a system dynamics model (SDM group), an agent-based model (ABM group), or both models (SDM & ABM group). This experimental design allowed learning outcomes (environmental and system dynamics knowledge, and understanding of the socio-environmental system) and use of the model(s) (in terms of the proportion of time spent on each screen, activities, and strategies) to be compared in each learning environment (individual and collaborative). Multiple external representations were the most successful strategy in the individual learning environment in terms of increases in environmental knowledge. However, students given only the system dynamics model had greater understanding of the system, and students given only the agent-based model increased environmental knowledge easily identified in the animated representation. Prior knowledge, patterns of use, strategies for changing variables and the representational affordances of the models explained some of these differences. In particular, prior knowledge was an important indicator of how students coordinated use of the models in the SDM & ABM group. Learning with a system dynamics model was the most successful strategy for students in the collaborative learning environment. Differences between the learning environments were detected in all groups with respect to both learning outcomes and use of the models due to prior knowledge, interrogation of the models, and the learning environments themselves. These experiments have provided evidence that strategies for understanding complex systems provide viable methods of communicating complex ideas to school-aged students with varying levels of prior knowledge. In particular, multiple external representations provided students with flexibility in how they learned; models allowed students to experiment with a system otherwise not allowed; and a collaborative learning environment facilitated students’ interpretation of a system dynamics model.

When autonomously controlling physical objects, a deviation from a trajectorycan lead to unwanted impacts, which can be very expensive or even dangerous. Thedeviation may be due to uncertainties, either from disturbance or model mismatch.One way to deal with these types of uncertainties is to design a robust control sys-tem, which creates margins for errors in the system. These margins make the systemsafe but also lowers the performance, hence it is desirable to have the margins assmall as possible and still make the system safe. One way to reduce the margins isto add a learning strategy to the control system, which improves the model repre-sentation using previous data. In this thesis, we investigate a robust control systemcalled tube-based model predictive control and then combine it with an adaptivegain scheduling method as the learning strategy. The adaptive feature in the gainscheduling method reduces the model mismatch between the model representationand the true dynamics by tuning the control parameters in the gain schedule usinga data-driven framework. To test this design, a dot is controlled to follow a pathin a constrained environment, around an obstacle. The dot should complete thetrack repeatedly without violating any constraints or crash into the obstacle whilereducing the model mismatch. Our results show that the error from the modelmismatch decreases with time without the dot touching the obstacle or moving out-side the constraints. As the error decreases, the margins in the controller becomesmaller, which makes it possible to control the system in a more efficient way andstill guarantee that the system remain safe.

Approved for public release; distribution is unlimited
One of the most important missions all Navies have is to constantly and sufficiently monitor their area of responsibility. This task becomes more challenging when a surveillance system operates in a complex environment with high traffic of merchant and fishing vessels and the existence of many islands. Potential tactics that targets might use increase the difficulty of this task. Integrating Unmanned Aerial Vehicles (UAVs) into a surveillance system that consists of ground radars and surface ships might enhance the systems capabilities and mitigate its vulnerabilities. In this study, the extremely complex maritime environment of the Aegean Sea is modeled in the Map Aware Non Uniform Automata (MANA) agent-based simulation environment to explore the effectiveness of UAVs in those conditions. The results from almost 100,000 simulated Intelligence, Surveillance, and Reconnaissance missions are analyzed using descriptive statistics, ANOVA, stepwise regression, and partition trees. It was found that by integrating one or two UAVs into a traditional surveillance system, it becomes more efficient in the detection and persistent surveillance of enemies and neutral targets. The most important factors that affect the surveillance systems performance are the detection capabilities of its sensors, the communication accuracy, and the enemys counter-detection capability. Thus, Greece and other countries with similar geographical characteristics should deploy UAVs in a maritime surveillance role.

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.
Includes bibliographical references (p. 125-136).
The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months has generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify self- report survey data, and study social network dynamics. By combining theoretical models with rich and systematic measurements, we show it is possible to gain insight into the underlying behavior of complex social systems.
by Nathan Norfleet Eagle.
Ph.D.

This work presents a framework for the inclusion of multiple criteria in the design process of supervised learning algorithms; as well as studies the sophisticated interactions among them. The criteria included and tested experimentally in this thesis are: accuracy, model complexity, algorithmic complexity, diversity and robustness. The present thesis addresses important challenges related to considering multiple criteria such as: 1) defining suitable measures for the included criteria, 2) determining effective approaches to optimise the system performance using multiple objectives, 3) finding effective alternative approaches to include such criteria indirectly in the design stages when defining accurate measures is infeasible, and finally 4) analysing the possible interactions among the criteria as well as identifying the main factors/decision points that modulate them. This work introduces a novel Multi-Components, Multi-Layer Predictive System (MCMLPS). This system incorporates mechanisms designed to control the diversity, model complexity and robustness. In the first stage of this thesis, the accuracy, model and algorithmic complexities of the base components for the proposed system have been optimised empirically using two multi-objective optimisation approaches. The first approach consists of a scalarized multi-objective optimisation, where the models are generated from optimising a single cost function that combines the three criteria. The second approach uses a Pareto-based multi objective optimisation which establishes a trade-off among the three criteria to generate a set of selectively balanced models. These first results showed that models generated from Pareto-based multi objective optimisation approach are both more accurate and more diverse than the models generated from scalarized multi-objective optimisation approach. However, the Pareto-based approach is hindered by the high algorithmic complexity required to find the best model and the infeasibility of defining universal measures for some of the above-mentioned criteria. Thus, in later stages of this work these criteria are either presented as constraints or included indirectly in generating the base components for the MCMLPS. In a subsequent stage of this study, the diversity among the base components of the proposed MCMLPS system is encouraged by training them on local regions in the data, were the locality is determined using the similarity of the data features. Each local region contains either disjoint subsets of the data and/or subsets of the features. A range of similarity metrics such as pairwise squared correlation and conditional mutual information of the features are used. Interestingly, the squared correlation method can be applied in supervised as well as unsupervised learning as it does not consider the output class when splitting the data. Meanwhile, the conditional mutual information method can be applied only in supervised learning as it uses the output class in splitting the data. The full MCMLPS architecture is then analysed and its performance is compared to three well-known ensemble methods. Next, the effect of weighing the components of the MCMLPS and combining them is examined using six fusion methods. The results showed that, including the similarity metric used to divide the data into local regions in weighing the system components, of- ten results in the best accuracy compared to the other fusion methods. In the final phase of this study, the robustness of the proposed system in noisy environments is tested and compared to other ensemble methods. The system showed a comparable accuracy to the best performing ensemble and it often has a more robust performance than other ensembles in highly noisy environments. To conclude, the present thesis proposes a multi-component, multi-layer system which simultaneously incorporates multiple criteria in its design cycle. The results of this thesis suggest that the locality in learning and high diversity among the components of the proposed system can be particularly beneficial in designing ensemble learning methods for highly noisy data sets.

The development of computational power is constantly on the rise and makes for new possibilities in a lot of areas. Two of the areas that has made great progress thanks to this development are control theory and artificial intelligence. The most eminent area of artificial intelligence is machine learning. The difference between an environment controlled by control theory and an environment controlled by machine learning is that the machine learning model will adapt in order to achieve a goal while the classic model needs preset parameters. This supposedly makes the machine learning model more optimal for an environment which changes over time. This theory is tested in this paper on a model of an inverted pendulum. Three different machine learning algorithms are compared to a classic model based on control theory. Changes are made to the model and the adaptability of the machine learning algorithms are tested. As a result one of the algorithms were able to mimic the classic model but with different accuracy. When changes were made to the environments the result showed that only one of the algorithms were able to adapt and achieve balance.

A complex system is one with many parts, whose behaviors are strongly dependent on each other. There are two interesting questions about complex systems. One is to understand how to recover the true structure of a complex system from noisy data. The other is to understand how the system interacts with its environment. In this thesis, we address these two questions by studying two distinct complex systems: dynamic systems and market microstructure. To address the first question, we focus on some nonlinear dynamic systems. We develop a novel Bayesian statistical method, Gaussian Emulator, to estimate the parameters of dynamic systems from noisy data, when the data are either fully or partially observed. Our method shows that estimation accuracy is substantially improved and computation is faster, compared to the numerical solvers. To address the second question, we focus on the market microstructure of hidden liquidity. We propose some statistical models to explain the hidden liquidity under different market conditions. Our statistical results suggest that hidden liquidity can be reliably predicted given the visible state of the market.
Statistics

Recent events have probably lead us to wonder why people make decisions that seem to be irrational, and that go against any easily understandable logic. The fact that these decisions are emotionally driven often explains what, at first glance, does not have a plausible explanation. Evidence has been found that proves that emotions and other affective characteristics guide decisions beyond a purely rational deliberation. Understanding the way emotions take place, the way emotions change, and/or the way emotions influence behavior, has traditionally been a concern of several fields including psychology and neurology. Moreover, other sciences such as behavioral economics, artificial intelligence, and in general, all sciences that aim to understand, explain, or simulate human behavior, acknowledge the important role of affective characteristics in this task. Specifically, artificial intelligence uses psychological findings in order to create agents that simulate human behavior. Nevertheless, individual research efforts in modeling affective characteristics are often overlapped, short of integration, and they lack of a common conceptual system. This deprives individual researches of the exchange and cooperation's inherent benefits, and makes the task of computationally simulating affective characteristics more difficult. Although much individual effort has been put in classifying, formalizing and modeling emotions and emotion theories on some fields, recognized researchers of emotions' and affective processes' modeling report that a common formal language, an informal conceptual system, and a general purpose affective agent architecture will greatly improve the interdisciplinary exchange and the intradisciplinary coordination. The research literature proposes a wide amount of affective models that deal with some of: relationship between emotions and cognition, relationship between emotions and behavior, emotions and their evolutionary account, emotions for appraising situations, emotion regulation, etc. These models are useful tools for addressing particular emotion-related issues. Furthermore, computational approaches that are based on particular psychological theories have also been proposed. They often address domain specific issues starting from a specific psychological theory. In such solutions, the absence of a common conceptual system and/or platform, makes difficult the feedback between psychological theories and computational approaches. This thesis systematizes and formalizes affect-related theories, what can benefit the interdisciplinary exchange, the intradisciplinary coordination, and hence, allows the improvement of involved disciplines. Specifically this thesis makes the following contributions: (1) a theoretical framework that includes the main processes and concepts that a model of an affective agent with practical reasoning should have; (2) a general-purpose affective agent architecture that shares the concepts of the proposed theoretical framework; (3) an implementation-independent formal language for designing affective agents that have the proposed architecture; and (4) a specific agent language for implementing affective agents which is an extension of a BDI language. Some studies with human participants have helped to validate the contributions of this thesis. They include classical games of game theory, and an study with 300 participants, which have provided the necessary information to evaluate the contributions. The validation has been performed in three directions: determine whether the proposed computational approach represents better the human behavior than traditional computational approaches; determine whether this approach allows to improve psychological theories used by default; and determine whether the proposed affective agents' behavior is closer to human behavior than the behavior of a purely rational agent.
Probablemente algunos eventos recientes nos han conducido a preguntarnos por qué las personas toman decisiones aparentemente irracionales y en contra de alguna lógica fácilmente comprensible. El hecho de que estas decisiones estén bajo la influencia de las emociones a menudo explica lo que, a primera vista, parece no tener una explicación aceptable. En este sentido, se han encontrado evidencias que prueban que las emociones y otras características afectivas condicionan las decisiones más allá de una deliberación meramente racional. Entender cómo las emociones tienen lugar, cómo cambian y cómo influyen en el comportamiento, ha sido tradicionalmente de interés para muchos campos de investigación, incluyendo la psicología y la neurología. Además, otras ciencias como la economía conductual o la inteligencia artificial reconocen el importante papel de las características afectivas en esta tarea. Específicamente, la inteligencia artificial utiliza los resultados obtenidos en psicología para crear agentes que simulan el comportamiento humano. Sin embargo, a menudo los esfuerzos individuales de investigación en el modelado del afecto se solapan, carecen de la suficiente integración y de un sistema conceptual común. Esto limita a las investigaciones individuales para disponer de los beneficios que ofrecen el intercambio y la cooperación, y hace más compleja la tarea de simular los procesos afectivos. Las emociones y teorías relacionadas han sido clasificadas, formalizadas y modeladas. No obstante, reconocidos investigadores argumentan que un lenguaje formal común, un sistema conceptual informal y una arquitectura de agentes de propósito general, mejorarán significativamente el intercambio interdisciplinar y la coordinación intradisciplinar. En la literatura se propone una amplia cantidad de modelos afectivos que modelan: la relación entre las emociones y la cognición, la relación entre las emociones y el comportamiento, las emociones para evaluar las situaciones, la regulación de emociones, etc. Estos modelos son herramientas útiles para abordar aspectos particulares relacionados con las emociones. Además, se han realizado propuestas computacionales que abordan aspectos específicos sobre la base de teorías psicológicas específicas. En éstas soluciones, la ausencia de una plataforma y/o sistema conceptual dificulta la retroalimentación entre las teorías psicológicas y las propuestas computacionales. Esta tesis sistematiza y formaliza teorías relacionadas con el afecto, lo cual beneficia el intercambio interdisciplinar y la coordinación intradisciplinar, y por tanto, permite el desarrollo de las disciplinas correspondientes. Específicamente esta tesis realiza las siguientes contribuciones: (1) una plataforma teórica que incluye los conceptos y procesos principales que debería poseer un modelo de agentes afectivos con razonamiento práctico; (2) una arquitectura de agentes de propósito general que comparte los conceptos de la plataforma teórica propuesta; (3) un lenguaje formal independiente de la implementación, para diseñar agentes afectivos que poseen la arquitectura propuesta; y (4) un lenguaje de agentes específico para implementar agentes afectivos el cual es un extensión de un lenguaje BDI. Algunos estudios con participantes humanos han ayudado a validar las contribuciones de esta tesis. Estos incluyen juegos clásicos de teoría de juegos y un estudio con 300 participantes, los cuales han proporcionado la información necesaria para evaluar las contribuciones. La validación se ha realizado en tres direcciones: determinar si la propuesta computacional que se ha realizado representa mejor el comportamiento humano que propuestas computacionales tradicionales; determinar si esta propuesta permite mejorar las teorías psicológicas empleadas por defecto; y determinar si el comportamiento de los agentes afectivos propuestos se acerca más al comportamiento humano que el compor
Probablement alguns esdeveniments recents ens han conduït a preguntar-nos per què les persones prenen decisions que aparentment són irracionals i que van en contra d'algun tipus de lògica fàcilment comprensible. El fet que aquestes decisions estiguin sota la influència de les emocions sovint explica el que, a primera vista, sembla no tenir una explicació acceptable. En aquest sentit, s'han trobat evidències que proven que les emocions i altres característiques afectives condicionen les decisions més enllà d'una deliberació merament racional. Entendre com les emocions tenen lloc, com canvien i com influeixen en el comportament, ha estat tradicionalment d'interès per a molts camps d'investigació, incloent la psicologia i la neurologia. A més, altres ciències com l'economia conductual, la intel·ligència artificial i, en general, totes les ciències que intenten entendre, explicar o simular el comportament humà, reconeixen l'important paper de les característiques afectives en aquesta tasca. Específicament, la intel·ligència artificial utilitza els resultats obtinguts en psicologia per crear agents que simulen el comportament humà. No obstant això, sovint els esforços individuals d'investigació en el modelatge de l'afecte es solapen, no tenen la suficient integració ni compten amb un sistema conceptual comú. Això limita a les investigacions individuals, que no poden disposar dels beneficis que ofereixen l'intercanvi i la cooperació, i fa més complexa la tasca de simular els processos afectius. Les emocions i teories relacionades han estat classificades, formalitzades i modelades. No obstant això reconeguts investigadors argumenten que un llenguatge formal comú, un sistema conceptual informal i una arquitectura d'agents de propòsit general, milloraran significativament l'intercanvi interdisciplinar i la coordinació intradisciplinar. En la literatura es proposa una àmplia quantitat de models afectius que modelen: la relació entre les emocions i la cognició, la relació entre les emocions i el comportament, les emocions per avaluar les situacions, la regulació d'emocions, etc. Aquests models són eines útils per abordar aspectes particulars relacionats amb les emocions. A més, s'han realitzat propostes computacionals que aborden aspectes específics sobre la base de teories psicològiques específiques. En aquestes solucions, l'absència d'una plataforma i/o sistema conceptual dificulta la retroalimentació entre les teories psicològiques i les propostes computacionals. Aquesta tesi sistematitza i formalitza teories relacionades amb l'afecte, la qual cosa beneficia l'intercanvi interdisciplinar i la coordinació intradisciplinar, i per tant, permet el desenvolupament de les disciplines corresponents. Específicament aquesta tesi realitza les següents contribucions: (1) una plataforma teòrica que inclou els conceptes i processos principals que hauria de posseir un model d'agents afectius amb raonament pràctic; (2) una arquitectura d'agents de propòsit general que comparteix els conceptes de la plataforma teòrica proposta; (3) un llenguatge formal independent de la implementació, per dissenyar agents afectius que posseeixen l'arquitectura proposada; i (4) un llenguatge d'agents específic per implementar agents afectius el qual és un extensió d'un llenguatge BDI. Alguns estudis amb participants humans han ajudat a validar les contribucions d'aquesta tesi. Aquests inclouen jocs clàssics de teoria de jocs i un estudi amb 300 participants, els quals han proporcionat la informació necessària per avaluar les contribucions. La validació s'ha realitzat en tres direccions: determinar si la proposta computacional que s'ha realitzat representa millor el comportament humà que propostes computacionals tradicionals; determinar si aquesta proposta permet millorar les teories psicològiques emprades per defecte; i determinar si el comportament dels agents afectius proposats s'acosta més al
Alfonso Espinosa, B. (2017). Agents with Affective Traits for Decision-Making in Complex Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90497
TESIS

Hypertext is the dominant method to navigate the Internet, providing user freedom and control over navigational behaviour. There has been an increase in converting existing educational material into Internet web pages but weaknesses have been identified in current WWW learning systems. There is a lack of conceptual support for learning from hypertext, navigational disorientation and cognitive overload. This implies the need for an established pedagogical approach to developing the web as a teaching and learning medium. Guided Discovery Learning is proposed as an educational pedagogy suitable for supporting WWW learning. The hypothesis is that a guided discovery environment will produce greater gains in learning and satisfaction, than a non-adaptive hypertext environment. A second hypothesis is that combining concept maps with this specific educational paradigm will provide cognitive support. The third hypothesis is that student learning styles will not influence learning outcome or user satisfaction. Thus, providing evidence that the guided discovery learning paradigm can be used for many types of learning styles. This was investigated by the building of a guided discovery system and a framework devised for assessing teaching styles. The system provided varying discovery steps, guided advice, individualistic system instruction and navigational control. An 84 subject experiment compared a Guided discovery condition, a Map-only condition and an Unguided condition. Subjects were subdivided according to learning styles, with measures for learning outcome and user satisfaction. The results indicate that providing guidance will result in a significant increase in level of learning. Guided discovery condition subjects, regardless of learning styles, experienced levels of satisfaction comparable to those in the other conditions. The concept mapping tool did not appear to affect learning outcome or user satisfaction. The conclusion was that using a particular approach to guidance would result in a more supportive environment for learning. This research contributes to the need for a better understanding of the pedagogic design that should be incorporated into WWW learning environments, with a recommendation for a guided discovery approach to alleviate major hypertext and WWW issues for distance learning.

The design and construction of American public high schools are forcibly influenced by ultra-cost effective techniques demanding simplicity in construction and durability of material. The inflexibility and banality of the architecture this paradigm typically delivers begs for exploration of the feasibility of innovative construction technologies. Technologies that influence both form and technique such as prefabrication of modular elements, utilization of CAD/CAM techniques to mill customized parts and pliable materials (i.e. plastics) crafted to achieve dynamic forms. More engaging, flexible learning environments could be realized that significantly increase the performance of the architecture, both formally and ecologically, as well as ennobling students.

This thesis advances the theory of network specialization by characterizing the effect of network specialization on the eigenvectors of a network. We prove and provide explicit formulas for the eigenvectors of specialized graphs based on the eigenvectors of their parent graphs. The second portion of this thesis applies network specialization to learning problems. Our work focuses on training reservoir computers to mimic the Lorentz equations. We experiment with random graph, preferential attachment and small world topologies and demonstrate that the random removal of directed edges increases predictive capability of a reservoir topology. We then create a new network model by growing networks via targeted application of the specialization model. This is accomplished iteratively by selecting top preforming nodes within the reservoir computer and specializing them. Our generated topology out-preforms all other topologies on average.

Function allocation is the design decision which assigns work functions to all agents in a team, both human and automated. Efforts to guide function allocation systematically has been studied in many fields such as engineering, human factors, team and organization design, management science, and cognitive systems engineering. Each field focuses on certain aspects of function allocation, but not all; thus, an independent discussion of each does not address all necessary issues with function allocation. Four distinctive perspectives emerged from a review of these fields: technology-centered, human-centered, team-oriented, and work-oriented. Each perspective focuses on different aspects of function allocation: capabilities and characteristics of agents (automation or human), team structure and processes, and work structure and the work environment. Together, these perspectives identify the following eight issues with function allocation: 1)Workload, 2)Incoherency in function allocations, 3)Mismatches between responsibility and authority, 4)Interruptive automation, 5)Automation boundary conditions, 6)Function allocation preventing human adaptation to context, 7)Function allocation destabilizing the humans' work environment, and 8)Mission Performance. Addressing these issues systematically requires formal models and simulations that include all necessary aspects of human-automation function allocation: the work environment, the dynamics inherent to the work, agents, and relationships among them. Also, addressing these issues requires not only a (static) model, but also a (dynamic) simulation that captures temporal aspects of work such as the timing of actions and their impact on the agent's work. Therefore, with properly modeled work as described by the work environment, the dynamics inherent to the work, agents, and relationships among them, a modeling framework developed by this thesis, which includes static work models and dynamic simulation, can capture the issues with function allocation. Then, based on the eight issues, eight types of metrics are established. The purpose of these metrics is to assess the extent to which each issue exists with a given function allocation. Specifically, the eight types of metrics assess workload, coherency of a function allocation, mismatches between responsibility and authority, interruptive automation, automation boundary conditions, human adaptation to context, stability of the human's work environment, and mission performance. Finally, to validate the modeling framework and the metrics, a case study was conducted modeling four different function allocations between a pilot and flight deck automation during the arrival and approach phases of flight. A range of pilot cognitive control modes and maximum human taskload limits were also included in the model. The metrics were assessed for these four function allocations and analyzed to validate capability of the metrics to identify important issues in given function allocations. In addition, the design insights provided by the metrics are highlighted This thesis concludes with a discussion of mechanisms for further validating the modeling framework and function allocation metrics developed here, and highlights where these developments can be applied in research and in the design of function allocations in complex work environments such as aviation operations.

This dissertation examines human settlement-size variation through the lens of hunter-gatherer archaeology. Research article 1 presents an analysis of prehistoric hunter-gatherer settlement patterns from a wide range of environmental contexts and in the absence of socioeconomic complexity. Hunter-gather settlement size variation is found to exhibit heavy-tailed statistical structure that is consistent with the statistical structure of modern settlement-size variation, supporting claims that socioeconomic complexity is not requisite for the formation of so-called settlement-size hierarchies in human societies. Following insights from hunter-gatherer anthropology, complex systems research, and ecology, research article 2 proposes that the structure of hunter-gatherer site-size variation is an emergent property of obligate tool use among mobile hunter-gatherers. As materials are moved, modified, and deposited on the landscape, they effectively subsidize the costs of future land use at those locations, which results in additional material deposition, attracting future use, and so on. Using an agent-based model, it is demonstrated that this recursive niche-construction behavior is sufficient to generate the heavy-tailed property of hunter-gatherer site-size variation. The working model is then used to predict other dimensions of hunter-gatherer settlement structure related to artifact clustering and site occupation histories. Research articles 2 and 3 present test results based on Late Archaic Period (7,000-5,000 B.P.) settlement patterns in the Lake Titicaca Basin, Peru. Good agreement is found between the predictions and empirical observations suggesting that ecological niche construction may have played a significant role in structuring hunter-gatherer mobility and land use, which in turn may have created a context for emergent settlement hierarchies.

Against the theoretical background of evolutionary behavioral economics, this project analyzes bounded rationality and adaptive behaviour in organizational settings characterized by complexity and persistent uncertainty. In particular, drawing upon the standard NK model, two laboratory experiments investigate individual and collective decision-making in combinatorial problems of resource allocation featuring multiple dimensions and various levels of complexity. In the first study, investment horizons of different length are employed to induce a near or distant future temporal orientation, in order to assess the effects of complexity and time horizon on performance and search behaviour, examine the presence of a temporal midpoint heuristic, and inspect the moderating effects of deadline proximity on the performance-risk relationship. This is relevant for organizational science because the passage of time is essential to articulate many strategic practices, such as assessing progress, scheduling and coordinating task-related activities, discerning the processual dynamics of how these activities emerge, develop, and terminate, or interpreting retrospected, current, and anticipated events. A greater or lesser amount of time reflects then a greater or lesser provision of resources, thereby representing a constraint that can greatly affect the ability to maintain a competitive advantage or ensure organizational survival. In the second study, the accuracy of the imitative process is varied to induce a flawless or flawed information diffusion system and, congruently, an efficient or inefficient communication network, in order to assess the effects of complexity and parallel problem-solving on autonomous search behaviour, clarify the core drivers of imitative behaviour, control for the degree of strategic diversity under different communication networks, and evaluate individual as well as collective performance conditional to the interaction between the levels of complexity and the modalities of parallel problem-solving. This is relevant for organizational science because imitating the practices of high-performing actors is one of the key strategies employed by organizations to solve complex problems and improve their performance, thereby representing a major part of the competitive process. The project is intended to contribute grounding individual and collective behaviour in a more psychologically and socially informed decision-making, with a view to further the research agenda of behavioral strategy and sustain the paradigm shift towards an evolutionary-complexity approach to real economic structures.

Sociotechnical systems (STSs) rely on the collaboration between humans and autonomous decision-making units to fulfill their objectives. Highly intertwined social and technical contextual factors influence the collaboration between these human and engineered elements, and consequently the performance characteristics of the STS. In the next two decades, the role allocated to STSs in our society will drastically increase. Thus, the effective design of STSs requires an improved understanding of the human-autonomy interdependency. This dissertation brings together management science along with systems thinking and uses a mixed-methods approach to investigate the interdependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work allocated to safety-critical decision makers and quantifies Rasmussen's workload boundary that represents the limit of attainable workload. The major contribution of this study is to quantify the qualitative theoretical construct of the workload boundary through a Pareto-Koopmans frontier. This frontier allows one to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their subjective preferences and contextual operational conditions. This study presents a novel integration of machine learning algorithms in an efficiency measurement framework to understand the influence of contextual factors. The results demonstrate that autonomous units successfully handle relatively simple operational conditions, while complex operational conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of efficiency measurement approaches that deal with the influence of contextual factors and their sensitivity to sample size. The results are organized in a structured taxonomy of their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design.
Doctor of Philosophy
A system is an integrated set of elements that achieve a purpose or goal. An autonomous system (ADS) is an engineered element that often substitutes for a human decision-maker, such as in the case of an autonomous vehicle. Sociotechnical systems (STSs) are systems that involve the collaboration of a human decision-maker with an ADS to fulfill their objectives. Historically, STSs have been used primarily for handling safety critical tasks, such as management of nuclear power plants. By design, STSs rely heavily on a collaboration between humans and ADS decision-makers. Therefore, the overall characteristics of a STS, such as system safety, performance, or reliability; is fully dependent on human decisions. The problem with that is that people are independent entities, who can be influenced by operational conditions. Unlike their engineered counterparts, people can be cognitively challenged, tired, or distracted, and consequently make mistakes. The current dependency on human decisions, incentivize business owners and engineers alike to increase the level of automation in engineered systems. This allows them to reduce operational costs, increase performance, and minimize human errors. However, the recent commercial aircraft accidents (e.g., Boeing 737-MAX) have indicated that increasing the level of automation is not always the best strategy. Given that increasing technological capabilities will spread the adoption of STSs, vast majority of existing jobs will either be fully replaced by an ADS or will change from a manual set-up into a STS. Therefore, we need a better understanding of the relationships between social (human) and engineered elements. This dissertation, brings together management science with systems thinking to investigate the dependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work handled by safety-critical decision makers in STSs. Primary contribution of this study is to use an analytic method to quantify the amount of work a person could safely handle within a STSs. This method also allows to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their preferences and operational conditions. This study presents a novel integration of machine learning algorithms to understand operational influences that propel a human-decision maker to handle the work manually or delegate it to ADSs. The results demonstrate that autonomous units successfully handle simple operational conditions. More complex conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of data-driven analytical management approaches that deal with the operational factors and investigates their sensitivity to sample size. The results are organized based on their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design.

Thesis (Ph. D.)--Public Policy, Georgia Institute of Technology, 2008.
Rogers, Juan D., Committee Chair ; Klein, Hans K., Committee Member ; Bolter, Jay David, Committee Member ; Nelson-Palmer, Mike, Committee Member ; Kingsley, Gordon, Committee Member.

Komplexe Systeme wie das Klima der Erde bestehen aus vielen Komponenten, die durch eine komplizierte Kopplungsstruktur miteinander verbunden sind. Für die Analyse solcher Systeme erscheint es daher naheliegend, Methoden aus der Netzwerktheorie, der Theorie dynamischer Systeme und dem maschinellen Lernen zusammenzubringen. Durch die Kombination verschiedener Konzepte aus diesen Bereichen werden in dieser Arbeit drei neuartige Ansätze zur Untersuchung komplexer Systeme betrachtet. Im ersten Teil wird eine Methode zur Konstruktion komplexer Netzwerke vorgestellt, die in der Lage ist, Windpfade des südamerikanischen Monsunsystems zu identifizieren. Diese Analyse weist u.a. auf den Einfluss der Rossby-Wellenzüge auf das Monsunsystem hin. Dies wird weiter untersucht, indem gezeigt wird, dass der Niederschlag mit den Rossby-Wellen phasenkohärent ist. So zeigt der erste Teil dieser Arbeit, wie komplexe Netzwerke verwendet werden können, um räumlich-zeitliche Variabilitätsmuster zu identifizieren, die dann mit Methoden der nichtlinearen Dynamik weiter analysiert werden können. Die meisten komplexen Systeme weisen eine große Anzahl von möglichen asymptotischen Zuständen auf. Um solche Zustände zu beschreiben, wird im zweiten Teil die Monte Carlo Basin Bifurcation Analyse (MCBB), eine neuartige numerische Methode, vorgestellt. Angesiedelt zwischen der klassischen Analyse mit Ordnungsparametern und einer gründlicheren, detaillierteren Bifurkationsanalyse, kombiniert MCBB Zufallsstichproben mit Clustering, um die verschiedenen Zustände und ihre Einzugsgebiete zu identifizieren. Bei von Vorhersagen von komplexen Systemen ist es nicht immer einfach, wie Vorwissen in datengetriebenen Methoden integriert werden kann. Eine Möglichkeit hierzu ist die Verwendung von Neuronalen Partiellen Differentialgleichungen. Hier wird im letzten Teil der Arbeit gezeigt, wie hochdimensionale räumlich-zeitlich chaotische Systeme mit einem solchen Ansatz modelliert und vorhergesagt werden können.
Complex systems such as the Earth's climate are comprised of many constituents that are interlinked through an intricate coupling structure. For the analysis of such systems it therefore seems natural to bring together methods from network theory, dynamical systems theory and machine learning. By combining different concepts from these fields three novel approaches for the study of complex systems are considered throughout this thesis. In the first part, a novel complex network construction method is introduced that is able to identify the most important wind paths of the South American Monsoon system. Aside from the importance of cross-equatorial flows, this analysis points to the impact Rossby Wave trains have both on the precipitation and low-level circulation. This connection is then further explored by showing that the precipitation is phase coherent to the Rossby Wave. As such, the first part of this thesis demonstrates how complex networks can be used to identify spatiotemporal variability patterns within large amounts of data, that are then further analysed with methods from nonlinear dynamics. Most complex systems exhibit a large number of possible asymptotic states. To investigate and track such states, Monte Carlo Basin Bifurcation analysis (MCBB), a novel numerical method is introduced in the second part. Situated between the classical analysis with macroscopic order parameters and a more thorough, detailed bifurcation analysis, MCBB combines random sampling with clustering methods to identify and characterise the different asymptotic states and their basins of attraction. Forecasts of complex system are the next logical step. When doing so, it is not always straightforward how prior knowledge in data-driven methods. One possibility to do is by using Neural Partial Differential Equations. Here, it is demonstrated how high-dimensional spatiotemporally chaotic systems can be modelled and predicted with such an approach in the last part of the thesis.

Using Learning Management Systems (LMSs) in educational environmentshas facilitated the communication between students and teachers, and raisednew challenges as well. The aim of this research is to investigate the role ofLMS in the learning and teaching processes from students and teachersperspectives. We adopted a social constructivist worldview. We used aninductive qualitative approach, a single case study design and hermeneuticalapproach for analyzing the interviews and observations. We used Garrison etal. (2000) community of inquiry framework as a theoretical guide for thestudy. The research took place at the School of Computer Science, Physicsand mathematics department at Linnaeus University, Växjö campus. Theparticipants of this research were students and teachers from two masterlevels within the program of Information Systems. The study results indicatedthat students and teachers were content with the usage of Blackboard inorganizing courses materials. Although, most teachers didn’t encourageinteractive and discussion activities on Blackboard, students expressed theneed for such activities to help them in constructing and building newmeanings.

This research proposes sets of design patterns of learning environments as an innovative approach towards an intelligent architectural design process. These patterns are based on teachers’ spatial and pedagogical use of their learning environments. The study is based in the desired condition that learning environments are expected to host learning technologies efficiently, to adapt to the fact that its life span is much longer than that of any technology within it, and to accommodate a variation of teaching modes and learning styles. In an effort to address these issues; calls for designing flexible learning spaces have emerged, as well as recommendations for alternative layouts. Yet, more challenging questions emerge; how efficiently do these technologies integrate with other systems in the classroom space? What should architects and facility planners consider for a successful systems’ integration which incorporates learning technologies in the design of the classroom space? And how can these spaces support variations in pedagogical practice. This study attempts to answer these questions by developing a pattern language to support the early design phases of a technology-rich learning environment. The study is qualitative in nature, and based on interviews with a sample of teachers at academic year Governor’s science and technology schools in Virginia. The researcher attempts to capture problems and challenges related to occupants’ performance within the physical boundaries of the classroom when learning technologies are in use. The variation of teaching-learning modes is taken into consideration. In this process, the researcher focuses on integration patterns of learning technologies with the envelope and the interior systems. The findings are then translated into the design language in the form of a pattern language at the building systems scale.
Ph. D.

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.

The use of robotics has recently seen significant growth in various domains such as unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid robots. However, one of the most important and essential capabilities required for long term autonomy, which is the ability to operate robustly and safely in real-world environments, in contrast to industrial and laboratory setup is largely missing. Designing robots that can operate reliably and efficiently in cluttered and changing environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e. robots with multiple actuators. On one hand, the dexterity offered by the kinematic redundancy allows the robot to perform dexterous manipulation tasks in complex environments, whereas on the other hand, such complex system also makes controlling and planning very challenging. To address such two interrelated problems, we exploit robot motion synthesis from three perspectives that feed into each other: end-pose planning, motion planning and motion adaptation. We propose several novel ideas in each of the three phases, using which we can efficiently synthesise dexterous manipulation motion for fixed-base robotic arms, mobile manipulators, as well as humanoid robots in cluttered and potentially changing environments. Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite for other modules such as motion planning, is an important and yet unsolved problem in robotics. Such information is often assumed given, or manually provided in practice, which significantly limiting high-level autonomy. In our research, by using novel data pre-processing and encoding techniques, we are able to efficiently search for collision-free end-poses in challenging scenarios in the presence of uneven terrains. After having found the end-poses, the motion planning module can proceed. Although motion planning has been claimed as well studied, we find that existing algorithms are still unreliable for robust and safe operations in real-world applications, especially when the environment is cluttered and changing. We propose a novel resolution complete motion planning algorithm, namely the Hierarchical Dynamic Roadmap, that is able to generate collision-free motion trajectories for redundant robotic arms in extremely complicated environments where other methods would fail. While planning for fixed-base robotic arms is relatively less challenging, we also investigate into efficient motion planning algorithms for high DoF (30 - 40) humanoid robots, where an extra balance constraint needs to be taken into account. The result shows that our method is able to efficiently generate collision-free whole-body trajectories for different humanoid robots in complex environments, where other methods would require a much longer planning time. Both end-pose and motion planning algorithms compute solutions in static environments, and assume the environments stay static during execution. While human and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To address this issue, we propose a novel state space representation, the Distance Mesh space, in which the robot is able to remap the pre-planned motion in real-time and adapt to environmental changes during execution. By utilizing the proposed end-pose planning, motion planning and motion adaptation techniques, we obtain a robotic framework that significantly improves the level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR (7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator), Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing that our methods are truly applicable to solve high dimensional motion planning for practical problems.

Though many researchers have suggested that 3D virtual environments (VEs) could provide advantages for conceptual learning, few studies have attempted to evaluate the validity of this claim. While many educational VEs share the challenge of providing learners with information within 3D spaces, few researchers have investigated what approaches are used to help learn new information from 3D spatial representations. It is not understood how well learners can take advantage of 3D layouts to help understand information. Additionally, although complex arrangements of information within 3D space can potentially allow for large amounts of information to be presented within a VE, accessing this information can become more difficult due to the increased navigational challenges.
Complicating these issues are details regarding display types and interaction devices used for educational applications. Compared to desktop displays, more immersive VE systems often provide display features (e.g., stereoscopy, increased field of view) that support improved perception and understanding of spatial information. Additionally, immersive VE often allow more familiar, natural interaction methods (e.g., physical walking or rotation of the head and body) to control viewing within the virtual space. It is unknown how these features interact with the types of spatial information presentations to affect learning.
The research presented in this dissertation investigates these issues in order to further the knowledge of how to design VEs to support learning. The research includes six studies (five empirical experiments and one case study) designed to investigate how spatial information presentations affect learning effectiveness and learner strategies. This investigation includes consideration for the complexity of spatial information layouts, the features of display systems that could affect the effectiveness of spatial strategies, and the degree of navigational control for accessing information. Based on the results of these studies, we created a set of design guidelines for developing VEs for learning-related activities. By considering factors of virtual information presentation, as well as those based on the display-systems, our guidelines support design decisions for both the software and hardware required for creating effective educational VEs.

Ph. D.

In this dissertation we present rule-based machine learning methods for solving problems with high-dimensional or complex datasets. We are applying decision tree methods on blood-based biomarkers and neuropsychological tests to predict Alzheimer’s disease in its early stages. We are also using tree-based methods to identify disparity in dementia related biomarkers among three female ethnic groups. In another part of this research, we tried to use rule-based methods to identify homogeneous subgroups of subjects who share the same risk patterns out of a heterogeneous population. Finally, we applied a network-based method to reduce the dimensionality of a clinical dataset, while capturing the interaction among variables. The results show that the proposed methods are efficient and easy to use in comparison to the current machine learning methods.

Thesis (M.S. in Modeling Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, September 2008.
Thesis Advisor(s): McCauley, Michael. "September 2008." Description based on title screen as viewed on November 5, 2008. Includes bibliographical references (p. 85-88). Also available in print.