Готові списки джерел за темами / Complex systems learning

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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Complex systems learning"

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ELMS, DAVID G. "LEARNING ABOUT COMPLEX SYSTEMS." Civil Engineering Systems 9, no. 3 (October 1992): 265–72. http://dx.doi.org/10.1080/02630259208970653.

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Wong, K. Y. Michael, S. Li, and Y. W. Tong. "Complex dynamics in learning systems." Physica A: Statistical Mechanics and its Applications 288, no. 1-4 (December 2000): 397–401. http://dx.doi.org/10.1016/s0378-4371(00)00436-2.

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Sterman, John D. "Learning In and About Complex Systems." Reflections: The SoL Journal 1, no. 3 (March 1, 2000): 24–51. http://dx.doi.org/10.1162/152417300570050.

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Sterman, John D. "Learning in and about complex systems." System Dynamics Review 10, no. 2-3 (1994): 291–330. http://dx.doi.org/10.1002/sdr.4260100214.

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Lawson, Hal A., Dawn Anderson-Butcher, Nancy Petersen, and Carenlee Barkdull. "Design Teams as Learning Systems for Complex Systems Change." Journal of Human Behavior in the Social Environment 7, no. 1-2 (January 2003): 159–79. http://dx.doi.org/10.1300/j137v07n01_11.

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Herrington, Jessica, Ted Maddess, Dominique Coy, Corinne Carle, Faran Sabeti, and Marconi Barbosa. "Learning Complex Texture Discrimination." Journal of Vision 18, no. 10 (September 1, 2018): 260. http://dx.doi.org/10.1167/18.10.260.

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Kim, Jaehun. "Increasing trust in complex machine learning systems." ACM SIGIR Forum 55, no. 1 (June 2021): 1–3. http://dx.doi.org/10.1145/3476415.3476435.

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Анотація:
Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that cultural/artistic objects such as music and videos are analyzed and served to users according to their preference, enabled through ML techniques. One of the most recent breakthroughs in ML is Deep Learning (DL), which has been immensely adopted to tackle such complex problems. DL allows for higher learning capacity, making end-to-end learning possible, which reduces the need for substantial engineering effort, while achieving high effectiveness. At the same time, this also makes DL models more complex than conventional ML models. Reports in several domains indicate that such more complex ML models may have potentially critical hidden problems: various biases embedded in the training data can emerge in the prediction, extremely sensitive models can make unaccountable mistakes. Furthermore, the black-box nature of the DL models hinders the interpretation of the mechanisms behind them. Such unexpected drawbacks result in a significant impact on the trustworthiness of the systems in which the ML models are equipped as the core apparatus. In this thesis, a series of studies investigates aspects of trustworthiness for complex ML applications, namely the reliability and explainability. Specifically, we focus on music as the primary domain of interest, considering its complexity and subjectivity. Due to this nature of music, ML models for music are necessarily complex for achieving meaningful effectiveness. As such, the reliability and explainability of music ML models are crucial in the field. The first main chapter of the thesis investigates the transferability of the neural network in the Music Information Retrieval (MIR) context. Transfer learning, where the pre-trained ML models are used as off-the-shelf modules for the task at hand, has become one of the major ML practices. It is helpful since a substantial amount of the information is already encoded in the pre-trained models, which allows the model to achieve high effectiveness even when the amount of the dataset for the current task is scarce. However, this may not always be true if the "source" task which pre-trained the model shares little commonality with the "target" task at hand. An experiment including multiple "source" tasks and "target" tasks was conducted to examine the conditions which have a positive effect on the transferability. The result of the experiment suggests that the number of source tasks is a major factor of transferability. Simultaneously, it is less evident that there is a single source task that is universally effective on multiple target tasks. Overall, we conclude that considering multiple pre-trained models or pre-training a model employing heterogeneous source tasks can increase the chance for successful transfer learning. The second major work investigates the robustness of the DL models in the transfer learning context. The hypothesis is that the DL models can be susceptible to imperceptible noise on the input. This may drastically shift the analysis of similarity among inputs, which is undesirable for tasks such as information retrieval. Several DL models pre-trained in MIR tasks are examined for a set of plausible perturbations in a real-world setup. Based on a proposed sensitivity measure, the experimental results indicate that all the DL models were substantially vulnerable to perturbations, compared to a traditional feature encoder. They also suggest that the experimental framework can be used to test the pre-trained DL models for measuring robustness. In the final main chapter, the explainability of black-box ML models is discussed. In particular, the chapter focuses on the evaluation of the explanation derived from model-agnostic explanation methods. With black-box ML models having become common practice, model-agnostic explanation methods have been developed to explain a prediction. However, the evaluation of such explanations is still an open problem. The work introduces an evaluation framework that measures the quality of the explanations employing fidelity and complexity. Fidelity refers to the explained mechanism's coherence to the black-box model, while complexity is the length of the explanation. Throughout the thesis, we gave special attention to the experimental design, such that robust conclusions can be reached. Furthermore, we focused on delivering machine learning framework and evaluation frameworks. This is crucial, as we intend that the experimental design and results will be reusable in general ML practice. As it implies, we also aim our findings to be applicable beyond the music applications such as computer vision or natural language processing. Trustworthiness in ML is not a domain-specific problem. Thus, it is vital for both researchers and practitioners from diverse problem spaces to increase awareness of complex ML systems' trustworthiness. We believe the research reported in this thesis provides meaningful stepping stones towards the trustworthiness of ML.
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Kempe, Vera, and Patricia J. Brooks. "Second Language Learning of Complex Inflectional Systems." Language Learning 58, no. 4 (December 2008): 703–46. http://dx.doi.org/10.1111/j.1467-9922.2008.00477.x.

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Allen, Peter M., and Mark Strathern. "Evolution, Emergence, and Learning in Complex Systems." Emergence 5, no. 4 (December 2003): 8–33. http://dx.doi.org/10.1207/s15327000em0504_4.

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Huang, Xueqin, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang, and Ailin Liang. "Parallel Learning of Dynamics in Complex Systems." Systems 10, no. 6 (December 15, 2022): 259. http://dx.doi.org/10.3390/systems10060259.

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Анотація:
Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment.
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Дисертації з теми "Complex systems learning"

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Sullivan, John P. "Emergent Learning: Three Learning Communities as Complex Adaptive Systems." Thesis, Boston College, 2009. http://hdl.handle.net/2345/663.

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Анотація:
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
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Attebo, Edvin. "Safe learning and control in complex systems." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-178164.

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Анотація:
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.
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Bondorowicz, Stefan. "Adaptive control of complex dynamic systems." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302787.

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Eagle, Nathan Norfleet. "Machine perception and learning of complex social systems." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32498.

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Анотація:
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.
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Al-Jubouri, Bassma. "Multi-criteria optimisation for complex learning prediction systems." Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/30857/.

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Анотація:
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.
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Tong, Xiao Thomas. "Statistical Learning of Some Complex Systems: From Dynamic Systems to Market Microstructure." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10917.

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Анотація:
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
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Passey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.

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Анотація:
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.
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CERBONI, BAIARDI LORENZO. "Adaptive models of learning in complex physical and social systems." Doctoral thesis, Urbino, 2016. http://hdl.handle.net/11576/2630552.

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Topcu, Taylan Gunes. "Management of Complex Sociotechnical Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97844.

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Анотація:
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.
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Urwin, Gerry. "Learning from complex information systems implementation : case studies in ERP projects." Thesis, Henley Business School, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268860.

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Книги з теми "Complex systems learning"

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Grigoʹevich, Ivakhnenko Alekseĭ, ed. Inductive learning algorithms for complex systems modeling. Boca Raton: CRC Press, 1994.

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1948-, Prete Frederick R., ed. Complex worlds from simpler nervous systems. Cambridge, Mass: MIT Press, 2004.

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Ruan, Da. Computational intelligence in complex decision systems. Paris: Atlantis Press, 2010.

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F, Chipman Susan, and Meyrowitz Alan L, eds. Foundations of knowledge acquisition: Cognitive models of complex learning. Boston: Kluwer Academic, 1993.

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1967-, Caballe Santi, ed. Architectures for distributed and complex M-learning systems: Applying intelligent technologies. Hershey, PA: Information Science Reference, 2010.

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6

1951-, Kirschner Paul Arthur, ed. Ten steps to complex learning: A systematic approach to four-component instructional design. Mahwah, N.J: Lawrence Erlbaum Associates, 2007.

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Merriënboer, Jeroen J. G. van. Ten steps to complex learning: A systematic approach to four-component instructional design. 2nd ed. New York: Routledge, 2012.

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Training complex cognitive skills: A Four-Component Instructional Design model for technical training. Englewood Cliffs, N.J: Educational Technology Publications, 1997.

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Societal Learning and Change: How Governments, Business and Civil Society are Creating Solutions to Complex Multi-Stakeholder Problems. London: Taylor and Francis, 2017.

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Societal learning and change: How governments, business and civil society are creating solutions to complex multi-stakeholder problems. Sheffield: Greenleaf Pub., 2005.

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Частини книг з теми "Complex systems learning"

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Macy, Michael W., Stephen Benard, and Andreas Flache. "Learning." In Understanding Complex Systems, 431–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-93813-2_17.

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Macy, Michael W., Steve Benard, and Andreas Flache. "Learning." In Understanding Complex Systems, 501–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66948-9_20.

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Moser, Hubert Anton. "Systems Engineering and Learning." In Understanding Complex Systems, 11–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03895-7_2.

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Helbing, Dirk. "Learning of Coordinated Behavior." In Understanding Complex Systems, 211–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24004-1_12.

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Nguyen, Dai Hai, Canh Hao Nguyen, and Hiroshi Mamitsuka. "Machine Learning for Metabolic Identification." In Creative Complex Systems, 329–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4457-3_20.

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Zanone, Pier-Giorgio, and Viviane Kostrubiec. "Searching for (Dynamic) Principles of Learning." In Understanding Complex Systems, 57–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39676-5_4.

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Tonsberg, Terje Andreas, and Jeffrey Shawn Henderson. "Some A Priori Aspects of Learning." In Understanding Complex Systems, 153–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40445-5_19.

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Rogers, Eric, Chris T. Freeman, Ann-Marie Hughes, Jane H. Burridge, Katie L. Meadmore, and Tim Exell. "Iterative Learning Control as an Enabler for Robotic-Assisted Upper Limb Stroke Rehabilitation." In Complex Systems, 157–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28860-4_8.

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Lee, Timothy D. "Intention in Bimanual Coordination Performance and Learning." In Understanding Complex Systems, 41–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39676-5_3.

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Dimirovski, G. M., A. Dourado, E. Ikonen, U. Kortela, J. Pico, B. Ribeiro, M. J. Stankovski, and E. Tulunay. "Learning Control of Thermal Systems." In Control of Complex Systems, 317–37. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0349-3_14.

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Тези доповідей конференцій з теми "Complex systems learning"

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Burdet, G. "Geometrical methods in learning theory." In Disordered and complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1358171.

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Buhot, Arnaud. "Storage capacity of the Tilinglike Learning Algorithm." In Disordered and complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1358157.

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Axtell, Travis, Lucas A. Overbey, and Lisa Woerner. "Machine learning in complex systems." In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, edited by Tien Pham, Michael A. Kolodny, and Dietrich M. Wiegmann. SPIE, 2018. http://dx.doi.org/10.1117/12.2309547.

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Hamagami, Tomoki, Takashi Shibuya, and Shingo Shimada. "Complex-Valued Reinforcement Learning." In 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.384789.

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Lorscheid, Iris. "Learning Agents for Human Complex Systems." In 2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW). IEEE, 2014. http://dx.doi.org/10.1109/compsacw.2014.73.

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Zhang, Song-bao, Jin-cai Huang, Wei-ming Zhang, and Zhong Liu. "Research on Parallel Decision Analyzing for Complex System of Systems." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258986.

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Rajeswaran, Aravind, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine. "Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations." In Robotics: Science and Systems 2018. Robotics: Science and Systems Foundation, 2018. http://dx.doi.org/10.15607/rss.2018.xiv.049.

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Ouali-Alami, Chaimae, Abdelali El Bdouri, Nisrine Elmarzouki, Ayoub Korchi, and Younes Lakhrissi. "Approaches to Design Complex Software Systems." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010740200003101.

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Johnson, Michael, and Betsy DiSalvo. "Learning about Complex Adaptive Systems in Makerspaces." In SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3478432.3499243.

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Dobre, Mihai S., and Alex Lascarides. "Exploiting action categories in learning complex games." In 2017 Intelligent Systems Conference (IntelliSys). IEEE, 2017. http://dx.doi.org/10.1109/intellisys.2017.8324210.

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Звіти організацій з теми "Complex systems learning"

1

Glaser, Donald A. Hierarchical Learning of Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, February 1996. http://dx.doi.org/10.21236/ada312476.

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Crawford, Lara S., and S. S. Sastry. Learning Controllers for Complex Behavioral Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada325516.

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Faissol, D. Learning Interactions in Complex Biological Systems. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1573143.

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Rupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1808822.

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Scheinberg, Katya. Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada622645.

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Silberstein, Jason, and Marla Spivack. Applying Systems Thinking to Education: Using the RISE Systems Framework to Diagnose Education Systems. Research on Improving Systems of Education (RISE), January 2023. http://dx.doi.org/10.35489/bsg-rise-ri_2023/051.

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Анотація:
This essay summarises a framework for understanding education systems by specifying the system’s components and the ways that those components interact to cultivate or undermine learning for children. Since education systems are complex and involve complex interactions, a structured framework for characterising their features can help identify problems and the way towards solutions to overcome them.
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Barjum, Daniel. PDIA for Systems Change: Tackling the Learning Crisis in Indonesia. Research on Improving Systems of Education (RISE), September 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/046.

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Indonesia is facing a learning crisis. While schooling has increased dramatically in the last 30 years, the quality of education has remained mediocre (Rosser et al., 2022). Teacher capability is an often cited weakness of the system, along with policies and system governance. Approaches focused primarily on adding resources to education have not yielded expected outcomes of increased quality. “It is a tragedy that in the second decade of the twenty-first century, some children in Indonesia are not completing primary school and are turned out into the workforce as functional illiterates.” (Suryadarma and Jones, 2013; Nihayah et al., 2020). In the early 2000s, Indonesia began a process of decentralising service delivery, including education, to the district level. Many responsibilities were transferred from the central government to districts, but some key authorities, such as hiring of civil service teachers, remained with the central government. The Indonesian system is complex and challenging to manage, with more than 300 ethnic groups and networks of authority spread over more than 500 administrative districts (Suryadarma and Jones, 2013). Niken Rarasati and Daniel Suryadarma researchers at SMERU, an Indonesian think tank and NGO, understood this context well. Their prior experience working in the education sector had shown them that improving the quality of education within the classroom required addressing issues at the systems level (Kleden, 2020). Rarasati noted the difference in knowledge between in-classroom teaching and the systems of education: “There are known-technologies, pedagogical theories, practices, etc. for teaching in the classroom. The context [for systems of education] is different for teacher development, recruitment, and student enrollment. Here, there is less known in the public and education sector.” Looking for ways to bring changes to policy implementation and develop capabilities at the district level, SMERU researchers began to apply a new approach they had learned in a free online course offered by the Building State Capability programme at the Center for International Development at Harvard University titled, “The Practice of PDIA: Building Capability by Delivering Results”. The course offered insights on how to implement public policy in complex settings, focused on using Problem Driven Iterative Adaptation (PDIA). The researchers were interested in putting PDIA into practice and seeing if it could be an effective approach for their colleagues in government. This case study reviews Rarasati and Suryadarma’s journey and showcases how they used PDIA to foster relationships between local government and stakeholders, and bring positive changes to the education sector.
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Hayes, Anne M., Eileen Dombrowski, Allison H. Shefcyk, and Jennae Bult. Learning Disabilities Screening and Evaluation Guide for Low- and Middle-Income Countries. RTI Press, April 2018. http://dx.doi.org/10.3768/rtipress.2018.op.0052.1804.

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Анотація:
Learning disabilities are among the most common disabilities experienced in childhood and adulthood. Although identifying learning disabilities in a school setting is a complex process, it is particularly challenging in low- and middle-income countries that lack the appropriate resources, tools, and supports. This guide provides an introduction to learning disabilities and describes the processes and practices that are necessary for the identification process. It also describes a phased approach that countries can use to assess their current screening and evaluation services, as well as determine the steps needed to develop, strengthen, and build systems that support students with learning disabilities. This guide also provides intervention recommendations that teachers and school administrators can implement at each phase of system development. Although this guide primarily addresses learning disabilities, the practices, processes, and systems described may be also used to improve the identification of other disabilities commonly encountered in schools.
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Kaffenberger, Michelle. The Role of Purpose in Education System Outcomes: A Conceptual Framework and Empirical Examples. Research on Improving Systems of Education (RISE), December 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/118.

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In many low- and middle-income countries that have achieved significant gains in learning outcomes, higher income and resources and greater knowledge of what to do to achieve learning cannot explain the differences in outcomes relative to lower performing countries. Such cases yield complex questions of “how” and “why” success was achieved. In this paper, I propose a conceptual framework for understanding drivers of education system performance and use it to argue that consensus-based commitment to the purpose of learning is a critical missing link to addressing the learning crisis. I then apply the conceptual framework to examples of successful system improvements. Finally, I propose efforts that can foster commitment to the purpose of learning in education systems.
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Hwa, Yue-Yi, and Lant Pritchett. Teacher Careers in Education Systems That Are Coherent for Learning: Choose and Curate Toward Commitment to Capable and Committed Teachers (5Cs). Research on Improving Systems of Education (RISE), December 2021. http://dx.doi.org/10.35489/bsg-rise-misc_2021/02.

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Анотація:
How can education authorities and organisations develop empowered, highly respected, strongly performance-normed, contextually embedded teaching professionals who cultivate student learning? This challenge is particularly acute in many low- and middle-income education systems that have successfully expanded school enrolment but struggle to help children master even the basics of reading, writing, and arithmetic. In this primer, we synthesise research from a wide range of academic disciplines and country contexts, and we propose a set of principles for guiding the journey toward an empowered, effective teaching profession. We call these principles the 5Cs: choose and curate toward commitment to capable and committed teachers. These principles are rooted in the fact that teachers and their career structures are embedded in multi-level, multi-component systems that interact in complex ways. We also outline five premises for practice, each highlighting an area in which education authorities and organisations can change the typical status quo approach in order to apply the 5Cs and realise the vision of empowered teaching profession.
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