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Integrating Ontologies into Multi-Agent Systems Engineering (MaSE) for University Teaching Environment

Prashant M. Dolia
Department Of Computer Science & Applications, Gaurishankar Lake Road, Bhavnagar University, Bhavnagar 364022 Gujarat. India
Abstract—Multi-agent systems have received much attention in recent years because of their many advantages in complex and distributed environments. There are a number of methodologies have been proposed for multiagent engineering process such as Multi-agent System Engineering (MaSE). I have used the MaSE engineering process for the development of my ontology based multiagent system for the Academic Institute. In the era of Semantic Web, the ontology has established as a powerful tool to enable knowledge sharing and it is an important means in Semantic Web to achieve the semantic interoperability among heterogeneous distributed systems. Both ontology and agent technologies are central to the semantic web, and their combined use will enable the sharing of heterogeneous, autonomous knowledge sources in a capable, adaptable and extensible manner. Ontology is used throughout the multi-agent system to assist the interactions among different agents as well as to improve the quality of the service provided by each agent. This paper focuses on the utilization of combining both Ontology and Multi-Agent System (MAS) structure towards system integration for University teaching environment. In this paper, I include system prototype for ontology based multi-agent system. I have used Web Ontology Language (OWL) for the development of domain ontology for the Academic Institute and KQML as an agent communication language. Finally I have developed ontology-based multiagent system for the university teaching environment by taking the benefits of both renowned technologies.

Index Terms—ontology, multi-agent systems, MaSE, OWL, KQML

Cite: Prashant M. Dolia, "Integrating Ontologies into Multi-Agent Systems Engineering (MaSE) for University Teaching Environment," Journal of Emerging Technologies in Web Intelligence, Vol. 2, No. 1, pp. 42-47, February 2010. doi:10.4304/jetwi.2.1.42-47
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