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very large repositories of learning objects. In this section wedescribe the case study in which this problem appears.Technological advances in the past few years, particularly inthe area of online delivery and e-learning, have inspiredchanges in the way educational materials are designed,developed, and delivered to teachers and students. A majorshift in educational materials development has occurred wherethere is a move away from the traditional method of developingcourses in an integrated way to accomplish a learningobjective to one that is based on the use of individual buildingblocks or bite-sized “learning objects” [8]. This approachresembles what Wayne Hodgins, Director of WorldwideLearning Solutions, has called the Legos TM approach. In thesame way that Legos TM building blocks can be used to build avariety of structures, so too learning objects can be used bylecturers, teachers and others in creative ways to build courseswhich meet different learning outcomes. The theoreticalunderpinning is that instead of thousands of people wastingtime “re-inventing the wheel”, a learning object onceconstructed can be re-used and shared. Steven Downs [9] hasargued that the economics of sharing learning objects arerelentless.This new approach has resulted in the establishment of alarge number of learning object repositories both withinAustralia and overseas.While theoretical underpinnings of learning objectrepositories are difficult to challenge, a number of issuesrelating to their establishment still need to be researched. Ourproject will address major problems/issues, such as the lack ofconsultation/analysis of user needs in the creation ofrepositories and inadequate resource dis covery tools. It hasbeen reported that many of the learning repositories aredifficult to use.We will try to address these problems:- by undertaking user needs and usages analysis, and byusing the data collected by the multi-agent component(see fig.1);- by facilitating access to the plethora of contentrepositories and to address the problems of locating,exploring and manipulating learning resources expertlyand creatively;- by using the Learning Object Exploration System, crossdomainsearching software and profiling systems toautomatically match the needs of users with theappropriate learning objects;- by using “intelligent” agents to “remember” frequentlyused and relevant resources and to inform usersthrough presenting more intelligently guided pathwayswithin that virtual environment.The project will deliver an intelligent Learning ObjectExploration System capable of identifying the needs ofteachers, lecturers and course builders. This “intelligence” willbe based on “profiling”, extensive user analysis and resourceassessment, and the construction of an “intelligent” agents toprovide appropriate feedback.The development of an intelligent learning architectureincorporates:- the ability to actively collect and access a wide rangeof content repositories- substantial improvements in the usefulness oflearning objects within any given repository leadingto enhanced exploitation of learning materials .- the provision of enhanced feedback for the bettermanagement of content repositories.III. MULTI-AGENT ARCHITECTUREIn this section we describe this architecture. An overall viewof the architecture is depicted in Fig.1.userInteracts withLearning Objects ExplorationSystemWebBrowserCommunicates withWebServerGives access toLearningObjectRepositoryLearningObjectProvides exploration assistancePlugs web contentsWritesLogLearningObjectReadsBusiness IntelligenceComponentArchitectureAccessesExplorationSystem InterfaceFig. 1. Overview of the whole systemAccessesStandard UserProfilesReadsAgents ComponentUser AgentsComponentNode AgentsComponentThe prototype is based on an agent infrastructure and agentframework developed in C#. The infrastructure supports multihostsdistribution via .Net’s remoting and MSMQ. PersistentMSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 61