In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Mu...In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.展开更多
One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions.These diseases can decimate crops,disrupt food supply chains...One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions.These diseases can decimate crops,disrupt food supply chains,and escalate the risk of food shortages,underscoring the urgency of implementing robust strategies to safeguard the world’s food sources.Deep learning methods have revolutionized the field of plant disease detection,offering advanced and accurate solutions for early identification and management.However,a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset.In this paper,we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops,even if the system was not trained on them.The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf,along with determining the disease’s prevalence rate on the entire leaf.For efficient classification and to leverage the excellence of the Inception model in disease recognition,we employ a small Inception model architecture,which is suitable for processing small regions without compromising performance.To confirm the effectiveness of our method,we trained and tested it using the widely acclaimed PlantVillage dataset,recognized as the most utilized dataset for its comprehensive and diverse coverage.Our method achieved an accuracy rate of 94.04%.Furthermore,when tested on new datasets,it achieved an accuracy rate of 97.13%.This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases.In addition,it outperformed the existing methods in its ability to identify any disease across any crop type,showcasing its potential for broad applicability and contribution to global food security initiatives.展开更多
文摘In recent years,researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions;yet this paradigm provides no capacity for inter-agent interaction.Multi-agent reinforcement learning(MARL)overcomes this limitation by allowing several agents to learn simultaneously within a shared environment,each choosing actions that maximize its own or the group's rewards.By explicitly modeling and exploiting agent-to-agent dynamics,MARL can align those interactions with pedagogical goals such as peer tutoring,collaborative problem-solving,or gamified competition,thus opening richer avenues for adaptive and socially informed learning experiences.This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance,engagement,equity,and reducing teacher workload compared to single agent or traditional approaches.It explores the educational domains and pedagogical problems addressed by MARL,identifies the algorithmic families used,and analyzes their influence on learning.The review also assesses experimental settings and evaluation metrics to determine ecological validity,and outlines current challenges and future research directions in applying MARL to education.
文摘One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions.These diseases can decimate crops,disrupt food supply chains,and escalate the risk of food shortages,underscoring the urgency of implementing robust strategies to safeguard the world’s food sources.Deep learning methods have revolutionized the field of plant disease detection,offering advanced and accurate solutions for early identification and management.However,a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset.In this paper,we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops,even if the system was not trained on them.The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf,along with determining the disease’s prevalence rate on the entire leaf.For efficient classification and to leverage the excellence of the Inception model in disease recognition,we employ a small Inception model architecture,which is suitable for processing small regions without compromising performance.To confirm the effectiveness of our method,we trained and tested it using the widely acclaimed PlantVillage dataset,recognized as the most utilized dataset for its comprehensive and diverse coverage.Our method achieved an accuracy rate of 94.04%.Furthermore,when tested on new datasets,it achieved an accuracy rate of 97.13%.This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases.In addition,it outperformed the existing methods in its ability to identify any disease across any crop type,showcasing its potential for broad applicability and contribution to global food security initiatives.
