An MARL-Based Distributed Learning Scheme for Capturing User Preferences in a Smart Environment

2017 ◽  
Author(s):  
Junsung Lim ◽  
Heesuk Son ◽  
Daekeun Lee ◽  
Dongman Lee
Keyword(s):  
Distributed Learning ◽  
User Preferences ◽  
Smart Environment ◽  
Learning Scheme

scholarly journals Distributed Learning Automata-based S-learning scheme for classification

2019 ◽  
Vol 23 (3) ◽  
pp. 1235-1250 ◽  
Author(s):  
Morten Goodwin ◽  
Anis Yazidi ◽  
Tore Møller Jonassen
Keyword(s):  
Learning Automata ◽  
Distributed Learning ◽  
Learning Scheme ◽  
Distributed Learning Automata

Collaborative Technology Impacts in Distributed Learning Environments

2009 ◽  
pp. 123-144
Author(s):  
Martha Grabowski ◽  
Greg Lepak ◽  
George Kulick
Keyword(s):  
Technology Acceptance ◽  
Learning Environments ◽  
Science Communication ◽  
Empirical Test ◽  
Distributed Learning ◽  
User Preferences ◽  
Design Features ◽  
Collaborative Technology ◽  
Collaborative Technologies ◽  
Distributed Learning Environments

Many studies have examined the impact of collaborative technology in distributed learning environments. Few of those studies involved new collaborative technologies such as mobile computing, and few were empirically tested. This chapter addresses the need to empirically examine the impacts of new collaborative technologies including mobile, wearable, embedded, and ubiquitous technologies, on distributed learners. The chapter introduces a technology-independent framework for considering collaborative technologies, including mobile technology; it relates expected technology impacts to user preferences using a generalizable research framework rooted in the social science, communication and technology acceptance literature. The framework is updated to include the lens of contextualization richness, and the results of an empirical test of the framework are presented. The results show user preferences for technologies with a high range of design features to support cognitive learning, while showing preference for technologies with a low range of design features to support perceived learning. Next steps and implications for future work conclude the chapter.

Investigating user preferences in night bus lines operation using Conjoint Analysis in the city of Thessaloniki, Greece

2020 ◽  
Author(s):  
Dimitrios Nalmpantis ◽  
Dimitra Giannaka ◽  
Stavros Malliaris ◽  
Evangelos Genitsaris ◽  
Ioannis Karagiotas ◽  
...  
Keyword(s):  
Conjoint Analysis ◽  
User Preferences ◽  
The City

DISPENSER MACHINE FOR BEVERAGES WITH USER PREFERENCES FROM HMI

2016 ◽  
Vol 5 (3) ◽  
pp. 25
Author(s):  
BODHALE ASMITA P. ◽  
KULKARNI J.S. ◽  
◽  
Keyword(s):  
User Preferences

A SMART ENVIRONMENT INFRASTRUCTURE

2016 ◽  
Vol 15 (10) ◽  
pp. 2285-2300
Author(s):  
Mihai Horia Zaharia
Keyword(s):  
Smart Environment

NEURAL NETWORK METHOD OF ADAPTING THE PARAMETERS OF THE INFORMATION SYSTEMS INTERFACE

2020 ◽  
pp. 26-31
Author(s):  
А.Д. Обухов ◽  
М.Н. Краснянский ◽  
М.С. Николюкин
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Statistical Processing ◽  
Low Complexity ◽  
User Preferences ◽  
Expert Assessment ◽  
Practical Implementation ◽  
Algorithmic Support ◽  
Interface Parameters

Рассматривается проблема выбора оптимальных параметров интерфейса в информационных системах с целью его персонализации под предпочтения пользователя и возможности его оборудования. В настоящее время для ее решения используется алгоритмическое обеспечение и статистическая обработка предпочтений пользователей, что не обеспечивает достаточной гибкости и точности. Поэтому в данной работе предлагается применение разработанного метода адаптации параметров интерфейса, основанного на анализе и обработке пользовательской информации с помощью нейронных сетей. Научная новизна метода заключается в автоматизации сбора, анализа данных и настройки интерфейса за счет использования и интеграции нейронных сетей в информационную систему. Рассмотрена практическая реализация предлагаемого метода на Python. Экспертная оценка адаптивности интерфейса тестовой информационной системы после внедрения разработанного метода показала его перспективность и эффективность. Разработанный метод показывает лучшую точность и низкую сложность программной реализации относительно классического алгоритмического подхода. Полученные результаты могут использоваться для автоматизации процесса выбора компонентов интерфейса различных информационных систем. Дальнейшие исследования заключаются в развитии и интеграции разработанного метода в рамках фреймворка адаптации информационных систем Here we consider the problem of choosing the optimal parameters of the interface in information systems with the aim of personalizing it for the preferences of the user and the capabilities of his equipment. Currently, algorithmic support and statistical processing of user preferences are used to solve it, which does not provide sufficient flexibility and accuracy. Therefore, in this work, we propose the application of the developed method for adapting interface parameters based on the analysis and processing of user information using neural networks. The scientific novelty of the method is to automate the collection, analysis of data and interface settings through the use and integration of neural networks in the information system. We consider the practical implementation of the proposed method in Python. An expert assessment of the adaptability of the interface of the test information system after the implementation of the developed method showed its availability and efficiency. The developed method shows the best accuracy and low complexity of software implementation relative to the classical algorithmic approach. The results obtained can be used to automate the selection of interface components for various information systems. Further research consists in the development and integration of the developed method within the framework of the information systems adaptation framework

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