scholarly journals On Passivity, Reinforcement Learning, and Higher Order Learning in Multiagent Finite Games

2021 ◽  
Vol 66 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Bolin Gao ◽  
Lacra Pavel
2013 ◽  
Vol 23 (1) ◽  
pp. 17-22 ◽  
Author(s):  
Michael S. Garver ◽  
Brian A. Roberts

2021 ◽  
Vol 52 (2) ◽  
pp. 24-27
Author(s):  
Sofoklis A. Sotiriou

Science classrooms (even in the time of the pandemic) should provide more challenging, inquiry-based, authentic and higher-order learning experiences allowing students to participate in scientific practices and tasks. Rich scientific databases, e-Learning tools and digital educational resources can serve as a catalyst for science learning. They can offer a better understanding of complex scientific research, making science understandable and interesting to the students.


2010 ◽  
pp. 189-212 ◽  
Author(s):  
Dennis Charsky

This chapter will make a connection between game genres, game characteristics, and constructivist teaching structures. Constructivist teaching structures, like open learning environments and anchored instruction, have the same aims as serious games – to facilitate higher order learning skills and knowledge. However, constructivist teaching structures are not games and serious games are grappling with how to design games and keep the fun and learning in perfect balance. Making connections between game genres and characteristics (where much of the fun resides) and teaching structures (where much of the learning resides) will highlight commonalities that can be taken advantage of in the design of good serious games – where learning and fun are in perfect balance.


2013 ◽  
Vol 9 (2) ◽  
pp. 171-176 ◽  
Author(s):  
Chandana Watagodakumbura

Authentic education provides a unique learning experience to individual learners, specifically by addressing their psychological and neurological needs. The assessment of learners is done through generic attributes that have more validity and relates to intrinsic learner characteristics that could last throughout the life span of the learner. Authentic education looks at the general term education more broadly and deeply, and from multiple perspectives. As the individual learners are identified uniquely through authentic education, it embraces diversity within the human species more broadly and meaningfully. Learners are encouraged to pursue higher-order learning sending them through a complete learning cycle; this engages learners deeply to the task and provides a lasting experience, enabling individuals to reach their full potential. Authentic education aims at providing personal development for individuals broadly, not merely a career development, while still paving a better way to map individual preferences to more suitable career paths. Through authentic education, we get to value human resources much more than related economic aspects, making a significant difference to our current approaches and focus; it has the promise to effect a significant positive social change towards a sustainable development. The purpose of this study is to discuss conceptualising authentic education, multiple perspectives, better educational outcomes, learners embracing diversity, higher order learning, individual characteristics to related career paths, holistic personal development, social change valuing human resources, and consistent and predictable social development.


2020 ◽  
Vol 7 (3) ◽  
pp. 382-394
Author(s):  
Kai Syng Tan

What could a visual-led approach to the learning and teaching of complex issues look like for a short online synchronous session? Through a playful performance-lecture exploring concepts in diversity, interdisciplinarity and social change entitled What could a neurodiversity-led 2050 look like?, this paper outlines the possibilities of visual-centred approach, using the ubiquitous Microsoft software PowerPoint (or open-sourced equivalents like Google Slides and Prezi). It seeks to contribute to discourses and practices around role of visual approaches in Higher Education (HE) to address ‘difficult’ topics like power and inequality in an engaging manner, and to empower learners as active participants, including those who may be think visually, such as dyslexic learners. Such approaches will be urgent in a reality characterised by profound socio-political injustice highlighted by Black Lives Matter (BLM), and amid a global pandemic, where teaching occurs online, and where learners and teachers alike may be short of time, attention and resources. Highlighting techniques and perspectives from art, film and neurodiversity, it invites the consideration of the PowerPoint performance-lecture as a simple yet engaging and responsive process for higher order learning and creative thinking. A secondary point of the article to call for HE to itself apply a degree of critical and creative thinking about its own position, to use self-knowledge to do better, in order to move forward. It welcomes feedback and challenges, and calls for the creation of yet more playful, innovative, visual-led approaches in the learning and teaching of complex issues in Higher Education.


2019 ◽  
Author(s):  
Charles Findling ◽  
Nicolas Chopin ◽  
Etienne Koechlin

AbstractEveryday life features uncertain and ever-changing situations. In such environments, optimal adaptive behavior requires higher-order inferential capabilities to grasp the volatility of external contingencies. These capabilities however involve complex and rapidly intractable computations, so that we poorly understand how humans develop efficient adaptive behaviors in such environments. Here we demonstrate this counterintuitive result: simple, low-level inferential processes involving imprecise computations conforming to the psychophysical Weber Law actually lead to near-optimal adaptive behavior, regardless of the environment volatility. Using volatile experimental settings, we further show that such imprecise, low-level inferential processes accounted for observed human adaptive performances, unlike optimal adaptive models involving higher-order inferential capabilities, their biologically more plausible, algorithmic approximations and non-inferential adaptive models like reinforcement learning. Thus, minimal inferential capabilities may have evolved along with imprecise neural computations as contributing to near-optimal adaptive behavior in real-life environments, while leading humans to make suboptimal choices in canonical decision-making tasks.


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