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Reading different types of texts requires the use of different reading strategies and approaches. Making reading an active, observable process can be very beneficial to struggling readers. A good reader interacts with the text in order to develop an understanding of the information before them.
As a result, the brain adapts to the challenge of reading. The process of reading involves most of the brain, especially an interconnection between visual areas and language areas; but also neural systems related to action, emotion, decision-making, and memory. [2] [3] The science of reading (SOR) is the discipline that studies reading. [4]
Simple reflex agent Learning agent. A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. [1] Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. [2]
Reciprocal teaching is an amalgamation of reading strategies that effective readers are thought to use. As stated by Pilonieta and Medina in their article "Reciprocal Teaching for the Primary Grades: We Can Do It, Too!", previous research conducted by Kincade and Beach (1996 ) indicates that proficient readers use specific comprehension strategies in their reading tasks, while poor readers do ...
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. [ 1 ] Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the ...
In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning agent on a given task, in cooperation or competition with one or more agents. These agents learn by trial-and-error, and researchers may choose to have the learning algorithm play the role of two or more of the different agents.
Learning sciences (LS) is the critical theoretical understanding of learning, [1] engagement in the design and implementation of learning innovations, and the improvement of instructional methodologies. LS research traditionally focuses on cognitive-psychological, social-psychological, cultural-psychological and critical theoretical foundations ...
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
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