Abstract. The World Wide Web offers a great availability of heterogeneous educational resources. This suggests the idea that such materials can be re-used in compose courses. In this paper we address this issue by proposing an architecture for composing teaching courses using "the best parts" of heterogeneous educational materials available on the Web. Course composition relies on a simple but effective evaluation methodology which reproduces real techniques used by teachers in composing and improving classroom courses. The final goal of this article is to help the teacher to construct his course until the obtension of a steady course.We present our initial work and discuss about future developments.
In the Semantic Web Services area, completing a complex request means calling many Web Services. They are characterized by their heterogeneity since they are build independently from the context in which they will be used. To compose them, we need to consider annotation and meta-data which will allow their characterization. The goal of this paper is to propose the execution of a formal model, allowing the automated processing of the composition of many Semantic Web Services. We define a request as a sequence of different Web Services with the help of a formal logic. We define the paths allowing its concrete resolution. Thanks to an abstraction of the Services whether they are viewed as physical, local or global, we propose an automation of the building of the request resolution map.
E-Learning or online learning refers to the use of computer technologies to design, create, deliver, manage and support learning for students and help teachers to provide their resources on the internet. And each web site contains sets of pages and associated indexes. To organize learning resources to facilitate the access to these resources by teachers or students, many useful queries and computations over such repositories involve traversal and navigation of the Web graph. In this paper we purposed learning resources cauterization by applying S-Node graph with respect in ontology or concept structure.
This paper addresses the architectural foundations of dynamic workflows in distributed multi-agent systems (MAS) integrated in Grid context. The purpose is to design an architecture at the same time taking into consideration tasks dependencies among agents, adaptation with respect to historic lessons learnt from past behaviour (memory) and the autonomous decisions when an unpredicted event occurs. In order to do this, given one ontology, called AGIO, which describes Agent-Grid Integration, we propose a workflow based on MAS with a complementary decision-making aid using Markov Logic Networks (MLN).
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