## 1 Introduction  
Autonomous, reactive, and proactive agents have social ability and can interact with other agents and humans to solve their problems. To perform their tasks and interact with each other, intelligent agents constitute systems called Multi-Agent Systems (MASs) [1]. In addition, autonomous agents can evaluate semantic data and collaborate with semantically defined entities of the Semantic Web, such as semantic web services (SWS), by using content languages [2]. The implementation of agent systems is naturally a complex task when considering their characteristics. The internal agent behavior model and any interaction within the agent organizations become even more complex and hard to implement when the requirements and the interactions for other agent environments such as the Semantic Web [3,4] are considered. Therefore, it is natural that methodologies are being applied to master the problem of defining such complex systems. One of the possible alternatives is represented by domain-specific modeling languages (DSMLs) [5; 6] that have notations and constructs tailored towards an application domain (e.g., MAS). DSMLs raise the abstraction level, expressiveness, and ease of use. The application of model-driven development (MDD) and use of DSMLs for MAS development emerged in agent-oriented software engineering (AOSE) research field especially for the last decade [7]. Researchers developed various metamodels (e.g., [8; 9; 10]) and DSMLs (e.g., [11; 12; 13; 14; 15]) to cope with the challenges encountered on design and implementation of MASs. Moreover, some fully fledged DSMLs (e.g., [16; 17]) exist for developing software agents especially working in semantic web environments where agents can handle the Semantic Web content on behalf of their human users and interact with other semantic web environment entities, such as SWS. One of these MAS DSMLs is Semantic Web Enabled Agent Modeling Language (SEA_ML) [17] which has a built-in support for the modeling interactions of agent and semantic web services by including several specialized viewpoints. SEA_ML aims to enable domain experts to model their own MASs on the Semantic Web without considering the limitations of using existing MAS development frameworks (e.g., JADE [18], JADEX [19] or JACK [20]). The evaluations [21], conducted for the assessment of SEA_ML, show promising results considering the generation performance and the development time reduction during MAS design and implementation. According to the experiences gained from the multi-case study [21] conducted by using SEA_ML, the developers can benefit more from this DSML when they use different viewpoints of SEA_ML in a proper way in the development of MAS. Therefore, in this study, we propose a model-driven MAS development methodology which is based on an extended version of SEA_ML and covers the whole process of analysis, modeling, code generation and fully implementation of a MAS working in the Semantic Web according to the well-known Belief-Desire-Intention (BDI) [22] agent principles. The use of the new SEA_ML-based MAS development methodology is exemplified with the development of a semantic web-enabled MAS for electronic bartering (E-barter). An agent-based E-barter system consists of agents that exchange goods or services of owners according to their preferences without using any currency. Although there are some studies developing agent-based E-barter systems such as [23; 24; 25; 26; 27; 28], none of them use BDI agents and their internal reasoning mechanism which can bring extra intelligence in the procedure of matchmaking for the E-bartering. Also, these studies do not use SWSs as the automatic selection mechanism for the categories. Finally, while the methodologies applied in above studies are mostly code-centric and do not consider MDD, our study benefits from MDD and uses a DSML and its tool for the rapid implementation of the MAS.