Mobile e-groupwork is playing an increasingly fundamental role in the field of mobile learning and cooperative work. One of the most outstanding problems in group management over time is the evolution of groups themselves, in terms of initial membership, profile analysis, as well as membership changes according to new interests, skills, expertise and goals. All such information can hardly be handled by individuals, especially in geographically distributed large teams. To cope with this problem, an architecture is proposed based on Deep Reinforcement Learning, a powerful machine learning technique for the automated management of resources.
MACHINE LEARNING-SUPPORTED MOBILE E-GROUPWORK MANAGEMENT
C De Castro
2020
Abstract
Mobile e-groupwork is playing an increasingly fundamental role in the field of mobile learning and cooperative work. One of the most outstanding problems in group management over time is the evolution of groups themselves, in terms of initial membership, profile analysis, as well as membership changes according to new interests, skills, expertise and goals. All such information can hardly be handled by individuals, especially in geographically distributed large teams. To cope with this problem, an architecture is proposed based on Deep Reinforcement Learning, a powerful machine learning technique for the automated management of resources.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.