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Applying Advisory Agents on the Semantic Web for E-Learning

Applying Advisory Agents on the Semantic Web for E-Learning

Ralf Bruns, Jürgen Dunkel, Sascha Ossowski
Copyright: © 2006 |Volume: 2 |Issue: 3 |Pages: 16
ISSN: 1548-3657|EISSN: 1548-3665|ISSN: 1548-3657|EISBN13: 9781615203703|EISSN: 1548-3665|DOI: 10.4018/jiit.2006070103
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MLA

Bruns, Ralf, et al. "Applying Advisory Agents on the Semantic Web for E-Learning." IJIIT vol.2, no.3 2006: pp.40-55. http://doi.org/10.4018/jiit.2006070103

APA

Bruns, R., Dunkel, J., & Ossowski, S. (2006). Applying Advisory Agents on the Semantic Web for E-Learning. International Journal of Intelligent Information Technologies (IJIIT), 2(3), 40-55. http://doi.org/10.4018/jiit.2006070103

Chicago

Bruns, Ralf, Jürgen Dunkel, and Sascha Ossowski. "Applying Advisory Agents on the Semantic Web for E-Learning," International Journal of Intelligent Information Technologies (IJIIT) 2, no.3: 40-55. http://doi.org/10.4018/jiit.2006070103

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Abstract

In this article, we present the software architecture of a new generation of advisory systems using Intelligent Agent and Semantic Web technologies. Multi-agent systems provide a well-suited paradigm to implement negotiation processes in a consultancy situation. Software agents act as clients and advisors, using their knowledge to assist human users. In the presented architecture, the domain knowledge is modeled semantically by means of XML-based ontology languages such as OWL. Using an inference engine, the agents reason, based on their knowledge to make decisions or proposals. The agent knowledge consists of different types of data: on the one hand, private data, which has to be protected against unauthorized access; and on the other hand, publicly accessible knowledge spread over different Web sites. As in a real consultancy, an agent only reveals sensitive private data, if they are indispensable for finding a solution. In addition, depending on the actual consultancy situation, each agent dynamically expands its knowledge base by accessing OWL knowledge sources from the Internet. Due to the standardization of OWL, knowledge models easily can be shared and accessed via the Internet. The usefulness of our approach is proved by the implementation of an advisory system in the Semantic E-learning Agent (SEA) project, whose objective is to develop virtual student advisers that render support to university students in order to successfully organize and perform their studies.

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