E-LEARNING SCENARIOS USING INTELLIGENT MULTIAGENT SYSTEMS

Journal of computer engineering and informatics, 2013

In the Dynamic Content Management (DCM) system one focuses on removing the tight association between learning material to specific courses. In this system one defines a conceptual atomic unit of knowledge and builds the material by the organization of these knowledge elements from a repository. The system allows an educator to create an arbitrary collection of knowledge elements, tag them with meta-information as a single unit and link the unit with previously saved and similar aggregations. The DCM system uses idea of Concept Maps to model the relationships between the knowledge elements that can be created by the instructor or the educator. A pilot system has been implemented based on a client-server architecture. The server is programmed in Python and the client as a Java application made available through Java Web Start. In the paper we also present different design and implementation aspects of a future Multi-Agent System (MAS) based on the DCM model. The implementation of such a model of e-learning is based on JADE-a FIPA compliant platform.

The Learning Organization, 2007

Purpose -The purpose of this paper is to illustrate the advantages of using intelligent agents to facilitate the location and customization of appropriate e-learning resources and to foster collaboration in e-learning environments. Design/methodology/approach -This paper proposes an e-learning environment that can be used to provide customized learning. It utilizes a set of interacting agents that can personalize instruction based on an individual's prior knowledge as well as their cognitive and learning needs. The e-learning agents monitor the e-learning environment and improve learning and collaboration based on learners' prior knowledge, social characteristics and learning style.

International Journal of Agent Technologies and Systems, 2015

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

In recent years, personalization and customization in cloud services are more than evident. Cloud e-Learning (CeL) is an upgraded model of traditional e-Learning which aims to offer personalization, collaboration through interaction and structured customized learning content. Cloud e-Learners will express their preferences and be offered courses as a result of a multi-agent system (MAS) interaction which will match learners' profiles and preferences with content of the learning cloud. The level of intelligence of the multi-agent system will continually increase as more learners interact with the system. Actual learning will be evaluated by agents and learners, thus providing opportunities to change preferences, domain interests and desired level of knowledge. Agents will be semantically assisted by ontologies. In this paper, we define a scenario for CeL and demonstrate the need for MAS and semantic annotation. We also present a tentative abstract architecture and speculate on future work.

In e-learning systems, currently present, the adaptive feedback and facilitation to student learning is very absolute. The teacher as well as students involved in distance learning education face a critical lapse towards proper development of skills as the present technologies are not sufficiently A.I.capable.We ought to propose a paradigm in reference to addition of soft computing technologies and agent based simulation in this paper. The technologies currently under implementation don’t possess the centralized controlling via artificially enhanced agents which can favour the suggestion process. The algorithms and methodologies used in this paper create an agent which is efficient. This paper gives a general description of technologies, contrasting them and provides a proposed solution to it.

Journal of Computer Science, 2007

We presented an agent-based e-learning environment. Our aim was to allow many users to interact collectively and intelligently with the environment. In this cooperation model, human users and artificial agents carry out tasks in the learners' service. We define the internal structure of our kernel supposed to work within Internet/Intranet settings. Design was structured in three parts: individual learning space, collaborative space, and cooperative space. We advocate the employment of an agent-based approach, a suitable for two main reasons: agents were a natural metaphor of human acts, and the learning systems are generally complex. Prometheus methodology used for the design and emphasis placed on the agent-based features.

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² WKH VR FDOOHG HDVVLVWDQWV RU KHOSHU SURJUDPV FDQ UHVLGH LQVLGH D FRPSXWHU DQG PDNH WKH OHDUQLQJ LQ HOHDUQLQJ RFFXU G\QDPLFDOO\ WR VXLW WKH QHHG RI WKH XVHU 7KH\ FDQ WUDFN WKH XVHU·V OLNHV DQG GLVOLNHV LQ GLIIHUHQW DUHDV WKH OHYHO RI NQRZOHGJH DQG WKH OHDUQLQJ VW\OH DQG DFFRUGLQJO\ UHFRPPHQG WKH EHVW PDWFKLQJ KHOSHUV IRU FROODERUDWLRQ 7KLV FKDSWHU RXWOLQHV WKH GHYHORSPHQW DQG WKH LPSOHPHQWDWLRQ SURFHVVHV RI D

Confluence 2013: The Next Generation Information Technology Summit (4th International Conference), 2013

The multi -agent system is one of the emerging trends being used today and the application of multi-agent systems is spreading in almost every field. It can provide better solutions to very complex and restrictive system. Here in this paper, we propose a new Multi agent based Architecture named I-MBLS (Interactive Multi-Agent Based Learning System) that we have designed for distance learning on the web. This architecture makes use of six different agents named Student Interface Agent (SIA)-responsible for monitoring student learning activity, Tutor Interface Agent (TIA)-responsible for providing an interactive interface to the tutor, Decision support Intelligent Agent (DSIA)responsible for important decision making, Collaborate agent (CA)-responsible for establishing collaboration among students, Test Agent( TA) -responsible for conducting tests, evaluating marks scored and lesson Planning agent (LPA)responsible for helping tutor for preparing a course content. These agents collaborate with each other and provide an elearning environment that is interactive, secure and efficient

International Journal of Emerging Technologies in Learning (iJET), 2009

The current life knows a remarkable development which changes all aspects of our life such as: our working environment, educational system, traveling patterns, sport activitiesâ?¦etc. The busy lifestyle faced by individuals today and the fast pattern of life may lead to their inability to be involved in a process of education. There is also a considerable technological development, especially the development of computing and communication technology. All these factors led to the idea of the need to reduce the time and to benefit from this technology, particularly in the field of education. The way of the classical education is very slow, and the student is obliged to be present at specific times which may be inappropriate for the majority of learners. Moreover, the substantial funds allocated for the success of classical learning process. Thus, it emerged the so-called E-learning via the Internet; the aims of this new mode of learning across the network (web) are to reduce the time ...

This short paper illustrates our approach to e -learning. We first consider the current challenges presented by e -learning (and the limitations of the traditional e -learning systems in fulfilling these needs). In the second section we describe our vision on how elearning systems could be designed in the future (we identify the features that e-learning systems should support). Finally we analyse how K-InCA, an agent-based system that we have designed to support the acquisition of Knowledge Management concepts and practices, can address the e-learning challenges responding to our vision.