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نتیجه جستجو - Learning theory

تعداد مقالات یافته شده: 11
ردیف عنوان نوع
1 Cooperative control for swarming systems based on reinforcement learning in unknown dynamic environment
کنترل مشارکتی برای سیستم های ازدحام مبتنی بر یادگیری تقویتی در یک محیط پویا ناشناخته-2020
This paper discussed the cooperative control problem for swarming systems in unknown dynamic environment. The swarm agents are required to move in a completely distributed manner with the reference trajectory determined by a virtual dynamic leader. In addition to keeping an appropriate distance from neighboring agents, each agent needs to avoid collision with dynamic threats in unknown environment. All of these complex requirements are integrated and designed as the performance index function for each agent. Then, the cooperative learning behavior of swarming system is realized by applying the reinforcement learning theory. Neural networks are used to model the control scheme and trained to minimize the performance index. The online updating rules of the neural networks are achieved based on the gradient descent algorithm. Finally, two simulation experiments are performed to verify the effectiveness of the cooperative control scheme and the environmental adaptability of the swarm agents.
Keywords: Swarming system | Cooperative control | Reinforcement learning | Neural networks | Dynamic threat
مقاله انگلیسی
2 The fit between market learning and organizational capabilities for management innovation
تناسب بین یادگیری بازار و قابلیت های سازمانی برای نوآوری مدیریت-2020
This paper examines how market learning (both explorative and exploitative) interacts with organizational capabilities (technological capabilities and marketing capabilities) to affect management innovation. Drawing upon data from a sample of 272 firms each of which contributed two key informants to the study (resulting in a total of 544 respondents), we find that both exploratory and exploitative market learning have a positive effect on management innovation. The effects of exploratory and exploitative market learning on management innovation are contingent on technological and marketing capabilities. Specifically, technological capabilities enhance the positive effect of exploratory market learning and weaken the positive effect of exploitative market learning on management innovation. Marketing capabilities enhance the positive effect of exploitative market learning and weaken the positive effect of exploratory market learning on management innovation. This study contributes to the literature by integrating organizational learning theory with the absorptive capacity perspective to explain management innovation.
Keywords: Management innovation | Market learning | Technological capabilities | Marketing capabilities
مقاله انگلیسی
3 Deep reinforcement learning for six degree-of-freedom planetary landing
یادگیری تقویتی عمیق برای فرود سیاره ای شش درجه آزادی-2020
This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and landing. Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse <5 m radius). This requires both a navigation system capable of estimating the lander’s state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander’s estimated state directly to a commanded thrust for each engine, resulting in accurate and almost fuel-optimal trajectories over a realistic deployment ellipse. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system’s performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.
Keywords: Reinforcement learning | Mars landing | Integrated guidance and control | Artificial intelligence | Autonomous maneuvers
مقاله انگلیسی
4 Dissociating the contributions of reward-prediction errors to trial-level adaptation and long-term learning
تفکیک سهم خطاهای پیش بینی پاداش در سازگاری در سطح کارآزمایی و یادگیری طولانی مدت-2020
Reward positivity (RewP) is an EEG component reflecting reward-prediction errors. Using multilevel models, we measured single-trial RewP amplitude from trial-to-trial, while reward and prediction varied during learning. Sixty participants completed a category-learning task in either engaging or sterile conditions with the RewP time-locked to feedback. Sequential analysis of single-trial RewP showed its relationship to current and previous accuracy, and the probability of changing one’s response to subsequent stimuli. Simulations show these effects can be explained in detail by the dynamics of participants’ expectations according to principles of reinforcement learning. The single-trial RewP findings were consistent with previous literature linking RewP to reward-prediction error under reinforcement-learning theory. In contrast, the aggregate RewP was unrelated to the engagement manipulation or to delayed retention performance. Thus the present results provide a detailed computational account how RewP relates to acute adaptation, but suggest RewP plays little role in long-term learning.
Keywords: EEG | Reinforcement learning | RewP | Adaptation
مقاله انگلیسی
5 The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach
نقش استفاده از اطلاعات در زنجیره تأمین خرده فروشی: رویکرد داده کاوی و مدل سازی تحلیلی-2019
This study utilizes both a resource-based view and organizational learning theory to present the need to distinguish information sharing from information usage. Our main research aim was to investigate the mediating role of information usage between information sharing, and operational efficiency and effectiveness. We tested the hypotheses in our relational model using empirical data obtained from food retailers in Turkey. The analysis of results from structural equation modeling reveal that the separation of information sharing from information usage is valid, and the mediating role of usage is significant in improving operational effectiveness and efficiency. We further utilized a Bayesian neural networks-based causal analytic model, i.e., universal structure modeling methodology to reveal non-trivial, implicit, previously unknown, and potentially useful relationships among the constructs.
Keywords: Information sharing | Information usage | Operational performance | Organizational learning | Data mining | Causal analytics
مقاله انگلیسی
6 Learning orientations and learning dynamics: Understanding heterogeneous approaches and comparative success in nascent entrepreneurship
جهت گیری های یادگیری و پویایی یادگیری: شناخت رویکردهای ناهمگن و موفقیت مقایسه ای در کارآفرینی نوپا-2019
Entrepreneurship is a learning process, yet the paths that entrepreneurs take to achieve success and the resources they assemble differ widely. To better understand when and for whom specific learning styles and new venture organizing activities are beneficial, this study develops a theoretical framework based on entrepreneurs learning orientations. We compare the founding trajectories of concrete experience and abstract conceptualization learner/entrepreneurs, as defined in experiential learning theory (ELT). The study tests the predictions with multinomial logit models. The results, using longitudinal data from the Panel Study of Entrepreneurial Dynamics, show that entrepreneurs who learn through sensory information and action benefit most from informal sources of capital and from their social networks, while those who learn by analyzing and systematically planning benefit most from formal sources of capital and from following their developed plans. The different trajectories that emerged in terms of capital formation and social network involvement should be of considerable interest to those attempting to either teach or promote entrepreneurship, as students and entrepreneurs undoubtedly have different learning requirements as well as pedagogical needs.
Keywords: Learning orientations | Experiential learning theory | Social networks | New venture financing | Second panel study of entrepreneurial dynamics | (PSED II)
مقاله انگلیسی
7 A Model-based Time-to-Failure Prediction Scheme for Nonlinear Systems via Deterministic Learning
یک طرح پیش بینی زمان تا عدم موفقیت مبتنی بر مدل برای سیستم های غیرخطی از طریق یادگیری تعیین کننده-2019
Time-to-failure (TTF) prediction is one of the most difficult problems in the area of prognostic and health management. In this paper, a new model-based TTF prediction scheme is proposed. Based on deterministic learning theory, a system dynamical pattern bank consisting of health, sub-health and fault patterns is established, and a set of estimators associated with the learned system patterns is used to generate average L1 norms of system residuals. Then, a TTF prediction model is derived based on the system residual generator with a predefined failure pattern. Once the first predicting time is obtained according to the incipient fault detection scheme, the system TTF can be predicted by projecting the learned fault dynamics at the current time against the failure threshold. Finally, an incipient fault detection and TTF prediction (IFDTP) algorithm is implemented by combining the established bank, the first predicting time and the TTF model. The novelty of this paper lies in that the new TTF prediction scheme can provide a more accurate system failure time for nonlinear dynamical systems, and the effectiveness of the proposed IFDTP algorithm is illustrated by simulation studies.
Keywords: Incipient Fault Detection | Prediction Model | Time to Failure | Dynamical Pattern Recognition | Deterministic Learning
مقاله انگلیسی
8 Soft extreme learning machine for fault detection of aircraft engine
یادگیری ماشین افراطی نرم برای تشخیص خطا موتور هواپیما-2019
When extreme learning machine (ELM) is used to cope with classification problems, the ±1is commonly used to construct the label vector. Since ELM adopts the square loss function, this means that it tends to force the margins of all the training samples exactly equaling one from the perspective of margin learning theory, which is unreasonable to some extent. To overcome this hard margin flaw, in this paper a soft extreme learning machine (SELM) is proposed, which flexibly sets a soft target margin for each training sample. Through solving a series of regularized ELMs (RELMs), SELM can be computed efficiently. Based on SELM, an improved SELM (ISELM) is proposed to deal with imbalanced classification problems, which can keep the same computational efficiency as SELM via solving a series of weighted RELMs. From the experimental results on benchmark data sets, the effectiveness and feasibility of SELM and ISELM are confirmed. More importantly, when they are applied to fault detection of aircraft engine, they are promising to be developed as the candidate techniques for it, and ISELM is especially in favor.
Keywords: Fault detection | Aircraft engine | Extreme learning machine | Imbalanced classification | Machine learning
مقاله انگلیسی
9 Statistics for big data: A perspective
آمار برای داده های بزرگ: یک چشم انداز-2018
We look at the role of statistics in data science. Two statisticians, two views. Besides the need of developing appropriate concepts, methodology and algorithms, the first one makes a case for validation and carefully designed simulation studies, while the second one writes that a mathematical underpinning of methods is fundamental. Both views converge to the same point: there should be more room for publishing negative findings.
Keywords: Heterogeneity ، Lasso ، Learning theory ، Negative results ، Replicability ، Reproducibility
مقاله انگلیسی
10 Moral Learning: Conceptual foundations and normative relevance
یادگیری اخلاقی: پایه های مفهومی و ارتباطات هنجاری-2017
What is distinctive about a bringing a learning perspective to moral psychology? Part of the answer lies in the remarkable transformations that have taken place in learning theory over the past two decades, which have revealed how powerful experience-based learning can be in the acquisition of abstract causal and evaluative representations, including generative models capable of attuning perception, cognition, affect, and action to the physical and social environment. When conjoined with developments in neuro science, these advances in learning theory permit a rethinking of fundamental questions about the acqui sition of moral understanding and its role in the guidance of behavior. For example, recent research indicates that spatial learning and navigation involve the formation of non-perspectival as well as ego centric models of the physical environment, and that spatial representations are combined with learned information about risk and reward to guide choice and potentiate further learning. Research on infants provides evidence that they form non-perspectival expected-value representations of agents and actions as well, which help them to navigate the human environment. Such representations can be formed by highly-general mental processes such as causal and empathic simulation, and thus afford a foundation for spontaneous moral learning and action that requires no innate moral faculty and can exhibit substan tial autonomy with respect to community norms. If moral learning is indeed integral with the acquisition and updating of casual and evaluative models, this affords a new way of understanding well-known but seemingly puzzling patterns in intuitive moral judgment—including the notorious ‘‘trolley problems.”
Keywords: Moral judgment | Moral development | Causal model | Evaluation | Simulation | Empathy | Bayesian | Reinforcement learning | Dual-process | Model-free and model-based learning and | control | Trolley problem
مقاله انگلیسی
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