Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering

2015 ◽  
pp. 356-364 ◽  
Author(s):  
Paula Rodríguez ◽  
Néstor Duque ◽  
Demetrio A. Ovalle
Keyword(s):  
Learning Objects ◽  
Multi Agent System ◽  
Agent System ◽  
Knowledge Based ◽  
Multi Agent

scholarly journals Multi-agent system for Knowledge-based recommendation of Learning Objects

2015 ◽  
Vol 4 (1) ◽  
pp. 80 ◽  
Author(s):  
Paula Andrea RODRÍGUEZ MARÍN ◽  
Néstor DUQUE ◽  
Demetrio OVALLE
Keyword(s):  
Learning Objects ◽  
Multi Agent System ◽  
Agent System ◽  
Knowledge Based ◽  
Multi Agent

An Adaptive Multi-Agent Environment

2011 ◽  
pp. 1-5
Author(s):  
Rejane Pinheiro ◽  
Elizabeth Furtado
Keyword(s):  
Collaborative Learning ◽  
Learning Objects ◽  
Decision Processes ◽  
Agent Technology ◽  
Artificial Agents ◽  
Multi Agent System ◽  
Agent System ◽  
Cognitive Characteristics ◽  
Construction Of Knowledge ◽  
Multi Agent

This article aims to develop a new environment of collaborative learning, by taking into account the criteria of construction of knowledge by the apprentices and the adaptative management of that knowledge by artificial agents. The multi-agent technology has been chosen due to the possibility of having artificial agents with internal decision processes to help students in the construction of their own projects and enabling learning objects available in accordance with the cognitive characteristics of the students and of their group. In this multi-agent system, exchanges of messages between the agents can occur so that they can perform theirs tasks in the best possible way.

VERIFICATION OF THE MOBILE AGENT NETWORK SIMULATOR — A TOOL FOR SIMULATING MULTI-AGENT SYSTEMS

2008 ◽  
Vol 18 (05) ◽  
pp. 651-682 ◽  
Author(s):  
MARIO KUSEK ◽  
KRESIMIR JURASOVIC ◽  
GORDAN JEZIC
Keyword(s):  
Mobile Agent ◽  
Formal Model ◽  
Task Complexity ◽  
Network Simulator ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Network Bandwidth ◽  
Knowledge Based ◽  
Multi Agent

This paper deals with the verification of a multi-agent system simulator. Agents in the simulator are based on the Mobile Agent Network (MAN) formal model. It describes a shared plan representing a process which allows team formation according to task complexity and the characteristics of the distributed environment where these tasks should be performed. In order to verify the simulation results, we compared them with performance characteristics of a real multi-agent system, called the Multi-Agent Remote Maintenance Shell (MA–RMS). MA–RMS is organized as a team-oriented knowledge based system responsible for distributed software management. The results are compared and analyzed for various testing scenarios which differ with respect to network bandwidth as well as task and network complexity.

Multi-Agent System for Recommending Learning Objects in E-Learning Environments

2021 ◽  
Author(s):  
Thais Oliveira Almeida ◽  
Jose Francisco De Magalhaes Netto ◽  
Arcanjo Miguel Mota Lopes
Keyword(s):  
Learning Environments ◽  
Learning Objects ◽  
Multi Agent System ◽  
Agent System ◽  
E Learning ◽  
Multi Agent

A Framework to Improve the Disaster Response Through a Knowledge-Based Multi-Agent System

2017 ◽  
Vol 9 (3) ◽  
pp. 96-109
Author(s):  
Claire Prudhomme ◽  
Christophe Cruz ◽  
Ana Roxin ◽  
Frank Boochs
Keyword(s):  
Decision Making ◽  
Information Exchange ◽  
Disaster Response ◽  
Explicit Representation ◽  
Multi Agent System ◽  
Agent System ◽  
Knowledge Based ◽  
Testing Strategies ◽  
Generic Agent ◽  
Multi Agent

The disaster response still faces problems of collaboration due to lack of policies concerning the information exchange during the response. Moreover, plans are prepared to respond to a disaster, but drills to apply them are limited and do not allow to determine their efficiency and conflicts with other organizations. This paper presents a framework allowing for different organizations involving in the disaster response to assess their collaboration through its simulation using an explicit representation of their knowledge. This framework is based on a multi-agent system composed of three generic agent models to represent the organizational structure of disaster response. The decision-making about response actions is done through task decomposition and repartition. It is based reasoning on ontologies which provides an explicit trace of the response plans design and their execution. Such framework aims at identifying cooperation problems and testing strategies of information exchange to support the preparation of disaster response.

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