A decay-based account of learning and adaptation in complex skills.

2021 ◽  
Author(s):  
Roderick Yang Terng Seow ◽  
Shawn A. Betts ◽  
John R. Anderson
Keyword(s):  
Complex Skills ◽  
Learning And Adaptation

scholarly journals Trans-complex education as a strategy in pandemic and post-pandemic COVID-19

2020 ◽  
pp. 27-28
Author(s):  
Juan Miguel González Velasco
Keyword(s):  
Curriculum Design ◽  
Educational Theory ◽  
Complex Approach ◽  
The Social ◽  
Complex Skills ◽  
Teachers And Students ◽  
Learning And Adaptation ◽  
Trans Complex ◽  
Self Learning

The trans-complex approach to education changes the work of the teacher and faculty, in particular the social outlook and resilience of the student is tested with this new mode of self-learning and adaptation. The new proposed form of education involves the application of a non-physical classroom, immersed in a creative and constant flux of complexity and trans-disciplinary processes; this is the social classroom born of the Trans-complex Educational Theory in response to the pandemic and post- pandemic phase of COVID-19. This case study will focus on the challenges to institutions, teachers and students, and relates to the struggle with acquiring new and complex skills. These struggles can be addressed using ‘R3 Education’ which promotes ‘Reinvention’, ‘Realignment’ and ‘Resilience’. The emergence of a responsive approach to curriculum design is now here - the trans-complex curriculum.

Dyadic protocols, observational learning, and the acquisition of complex skills

2003 ◽  
Author(s):  
Eric Anthony Day ◽  
Leigh E. Paulus ◽  
Winfred Arthur ◽  
Erich C. Fein
Keyword(s):  
Observational Learning ◽  
Complex Skills

scholarly journals Endogenous Agency Problems and the Dynamics of Rents

2020 ◽  
Vol 87 (6) ◽  
pp. 2542-2567
Author(s):  
B Biais ◽  
A Landier
Keyword(s):  
Overlapping Generations ◽  
Agency Problems ◽  
Overlapping Generations Model ◽  
Complex Tasks ◽  
Complex Skills ◽  
Do So

Abstract While potentially more productive, more complex tasks generate larger agency rents. Agents therefore prefer to acquire complex skills, to earn large rents. In our overlapping generations model, their ability to do so is kept in check by competition with predecessors. Old agents, however, are imperfect substitutes for young ones, because the latter are easier to incentivize, thanks to longer horizons. This reduces competition between generations, enabling young managers to go for larger complexity than their predecessors. Consequently, equilibrium complexity and rents gradually increase beyond what is optimal for the principal and for society.

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

Effects of learning and adaptation on population viability

2021 ◽  
Author(s):  
Naomi L. Indigo ◽  
Chris J. Jolly ◽  
Ella Kelly ◽  
James Smith ◽  
Jonathan K. Webb ◽  
...  
Keyword(s):  
Population Viability ◽  
Learning And Adaptation
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