predictive learning
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Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 164
Author(s):  
Yan Li ◽  
Mengyu Zhao ◽  
Huazhi Zhang ◽  
Yuanyuan Qu ◽  
Suyu Wang

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability.


2021 ◽  
Vol 4 ◽  
Author(s):  
Yu Huang ◽  
Yufei Tang ◽  
Hanqi Zhuang ◽  
James VanZwieten ◽  
Laurent Cherubin

According to the National Academies, a week long forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies at a given location is a critical step toward understanding their effects on the gulf ecosystems as well as toward anticipating and mitigating the outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM). However, creating such a forecast has remained a challenging problem since LC behavior is dominated by dynamic processes across multiple time and spatial scales not resolved at once by conventional numerical models. In this paper, building on the foundation of spatiotemporal predictive learning in video prediction, we develop a physics informed deep learning based prediction model called—Physics-informed Tensor-train ConvLSTM (PITT-ConvLSTM)—for forecasting 3D geo-spatiotemporal sequences. Specifically, we propose (1) a novel 4D higher-order recurrent neural network with empirical orthogonal function analysis to capture the hidden uncorrelated patterns of each hierarchy, (2) a convolutional tensor-train decomposition to capture higher-order space-time correlations, and (3) a mechanism that incorporates prior physics from domain experts by informing the learning in latent space. The advantage of our proposed approach is clear: constrained by the law of physics, the prediction model simultaneously learns good representations for frame dependencies (both short-term and long-term high-level dependency) and inter-hierarchical relations within each time frame. Experiments on geo-spatiotemporal data collected from the GoM demonstrate that the PITT-ConvLSTM model can successfully forecast the volumetric velocity of the LC and its eddies for a period greater than 1 week.


2021 ◽  
pp. 100060
Author(s):  
Anuradha Mathrani ◽  
Teo Susnjak ◽  
Gomathy Ramaswami ◽  
Andre Barczak

2021 ◽  
pp. 104529
Author(s):  
Gabriel González ◽  
José A. Alcalá ◽  
Pedro M. Ogállar ◽  
Juan M. Rosas ◽  
José E. Callejas-Aguilera

2021 ◽  
pp. 1-15
Author(s):  
Tom Olney ◽  
Steve Walker ◽  
Carlton Wood ◽  
Anactoria Clarke

Most higher education institutions view their increasing use of learning analytics as having significant potential to improve student academic achievement, retention outcomes, and learning and teaching practice but the realization of this potential remains stubbornly elusive. While there is an abundance of published research on the creation of visualizations, dashboards, and predictive models, there has been little work done to explore the impact of learning analytics on the actual practice of teachers. Through the lens of social informatics (an approach that views the users of technologies as active social actors whose technological practices constitute a wider socio-technical system) this qualitative study reports on an investigation into the practice of 30 tutors in the STEM faculty at Europe’s largest distance learning organization, The Open University UK (OU). When asked to incorporate learning analytics (including predictive learning analytics) contained in the Early Alert Indicator (EAI) dashboard during the 2017–2018 academic year into their practice, we found that tutors interacted with this dashboard in certain unanticipated ways and developed three identifiable “shadow practices”.


2021 ◽  
Vol 45 (8) ◽  
Author(s):  
Christian Gumbsch ◽  
Maurits Adam ◽  
Birgit Elsner ◽  
Martin V. Butz
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