scholarly journals A Cloud Information Monitoring and Recommendation Multi-Agent System with Friendly Interfaces for Tourism

2019 ◽  
Vol 9 (20) ◽  
pp. 4385
Author(s):  
Kune-Yao Chen ◽  
Sheng-Yuan Yang
Keyword(s):  
Recommendation System ◽  
Information Service ◽  
Open Data ◽  
Tourism Industry ◽  
Multi Agent System ◽  
Agent System ◽  
Service Architecture ◽  
Intelligent Decision Making ◽  
Information Monitoring ◽  
Multi Agent

The tourism statistics of Taiwan’s government state that the tourism industry is one of the fastest growing economic sources in the world. Therefore, the demand for a tourism information system with a friendly interface is growing. This research implemented the construction of a cloud information service platform based on numerous practical developments in the Dr. What-Info system (i.e., a master multi-agent system on what the information is), which developed universal application interface (UAI) technology based on the Taiwan government’s open data with the aim of connecting different application programming interfaces (APIs) according to different data formats and intelligence through local GPS location retrieval, in support of three-stage intelligent decision-making and a three-tier address-based UAI technology comparison. This paper further developed a novel citizen-centric multi-agent information monitoring and recommendation system for the tourism sector. The proposed system was experimentally demonstrated as a successful integration of technology, and stands as an innovative piece of work in the literature. Although there is room for improvement in experience and maybe more travel-related agents, the feasibility of the proposed service architecture has been proven.

scholarly journals Design and implementation of the multi-agent system in education

2021 ◽  
Vol 13 (4) ◽  
pp. 775-793
Author(s):  
Oussama Hamal ◽  
Nour-Eddine El Faddouli ◽  
Moulay Hachem Alaoui Harouni
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Educational Data Mining ◽  
Secondary Data ◽  
Primary Data ◽  
Multi Agent System ◽  
Agent System ◽  
Learning And Teaching ◽  
Wide Range ◽  
Multi Agent

Nowadays, AI is a real springboard for finding solutions to optimize and improve learning and teaching processes. This issue has been a focus of humanity for millennia, and very significant advances have been made in this quest. This article aims to address the issue of optimizing and improving learning and teaching processes through AI (Artificial Intelligence), considering crossroads of research fields AIED (Artificial Intelligence in Education), EDM (Educational Data Mining) and LA (Learning Analytic). The research made use of secondary data collected from previous research on the topic and primary data was collected using a case study. A comparative analysis was conducted and based on this opportunity, we propose a multi-agent system based on AI techniques, which is capable of performing broader analyses of learning and teaching processes. The research also implemented a prototype of EMAS. Through this system, teachers and learners will be able to access a wide range of relevant and reliable information about learning and teaching processes.   Key words: AIED, EDM, EMAS, LA, recommendation system, education dropping out, emotion detection

scholarly journals Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3597
Author(s):  
Luis Gomes ◽  
Carlos Almeida ◽  
Zita Vale
Keyword(s):  
Recommendation System ◽  
Context Aware ◽  
Multi Agent System ◽  
Confidence Bound ◽  
Agent System ◽  
Physical Context ◽  
Almost Everywhere ◽  
Multi Agent ◽  
Upper Confidence Bound ◽  
Best Workplaces

Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system.

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