Abstract
In agent-oriented software engineering (AOSE), the application of model-driven development (MDD) and the use of domain-specific modeling languages (DSMLs) for Multi-Agent System (MAS) development are quite popular since the implementation of MAS is naturally complex, error-prone, and costly due to the autonomous and proactive properties of the agents. The internal agent behavior and the interaction within the agent organizations become even more complex and hard to implement when the requirements and interactions for the other agent environments such as the Semantic Web are considered. Hence, in this study, we propose a model-driven MAS development methodology which is based on a domain-specific modeling language (called SEA_ML) and covers the whole process of analysis, modeling, code generation and implementation of a MAS working in the Semantic Web according to the well-known Belief-Desire-Intention (BDI) agent principles. The use of new SEA_ML-based MAS development methodology is exemplified with the development of a semantic web-enabled MAS for electronic bartering (E-barter). Achieved results validated the generation and the development-time performance of applying this new MAS development methodology. More than half of the all agents and artifacts needed for fully implementing the E-barter MAS were automatically obtained by just using the generation features of the proposed methodology.
Highlights
Autonomous, reactive, and proactive agents have social ability and can interact with other agents and humans to solve their problems
The codes generated by Semantic Web Enabled Agent Modeling Language (SEA_ML) are architectural codes and the relations are established by the language considering the model which are controlled by the language at the semantic control stage that prevents most of the semantic errors in the code
A development methodology is proposed for development of Multi-Agent System (MAS) working in semantic web environments
Summary
Autonomous, reactive, and proactive agents have social ability and can interact with other agents and humans to solve their problems. In the scope of this study, SEA_ML [17] is used as a DSML for the construction of semantic webenabled MASs. SEA_ML enables the developers to model the agent systems in a platform independent level and automatically achieve codes and related documents required for the execution of the modeled MAS on target MAS implementation platforms. To support MAS experts when programming their own systems, and to be able to fine-tune them visually, SEA_ML covers all aspects of an agent system from the internal view of a single agent to the complex MAS organization In addition to these capabilities, SEA_ML supports the model-driven design and implementation of autonomous agents who can evaluate semantic data and collaborate with semantically defined entities of the Semantic Web, such as SWSs. In addition to these capabilities, SEA_ML supports the model-driven design and implementation of autonomous agents who can evaluate semantic data and collaborate with semantically defined entities of the Semantic Web, such as SWSs Within this context, it includes new viewpoints which pave the way for the development of software agents working on the Semantic Web environment. Following subsections discuss the methodology’s phases covering those steps
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