Designing Distributed Learning Environments With Intelligent Software Agents by Fuhua Oscar Lin

Author:Fuhua Oscar Lin [Lin, Fuhua Oscar]
Language: eng
Format: epub
Tags: Computers, Intelligent agents (Computer software), Information science, Computer science, Intelligence (AI) & Semantics, Education, Computers & Technology, Artificial Intelligence, Intelligent tutoring systems
ISBN: 9781591405009
Publisher: Idea Group Inc (IGI)
Published: 2005-04-15T07:00:00+00:00

[2]Metadata is defined as “data about data” (Berners-Lee, 1997).

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Conclusions

In this chapter, we first addressed the issue of the important intelligence in MAS-based DLEs. We then emphasized three main intelligent competencies in MAS-based DLEs: intelligent decision-making support, coordination and collaboration of the agents in MAS, and student modeling for personalization and adaptation in learning systems. We also described in detail the application of relevant AI techniques, including the introduction of AI techniques, and their state-of-the-art applications in the e-learning domain.

For intelligent decision-making support, a knowledge-based system, that is, a rule-based reasoning technique, is suitable and feasible. The example presented, an intelligent learning environment for training navigators, distinctly exhibited the application of the rule-based reasoning technique to MAS-based DLEs for implementing intelligent support. This approach is also useful for other training applications, such as aviation training, firefighter training, and so on. For coordination and collaboration of agents in heterogonous MAS, CBR, particularly CCBR, is a good solution to implement for powerful coordinating and collaborating abilities. A MAS-based infrastructure that incorporates with a CBBR reasoner, NaCoDAE, to perform team-oriented coordination and collaboration was presented. SML techniques are effective for student modeling to create more accurate models and more useful knowledge. We found that most of the major SML techniques, no matter whether they are supervised learning or unsupervised learning, are constructive for building the student model or discovering the background knowledge for the student modeling. There is no best algorithm for a specific application, and it fully depends on the requirements from the applications.

Finally, future trends in the research and development of intelligence for MAS- based DLEs were discussed with regards to the development in the artificial intelligent research area. Four main trends, data mining to student modeling, integrated reasoning to intelligent support, a policy-based approach to negotiation between agents in heterogonous MAS, and ontology-based knowledge representation and management in DLEs were also addressed.

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