## 4 Conclusion
There are two groups of agents in this design. The Service Management Agent group including the Barter manager agent, SWS agent, and Cargo agent, which are responsible for managing the barter operations. The barter manager Agent is responsible for the management of the agent barter operations and matching. The SWS agent is responsible for the mapping based on the ontological proximity. The cargo agent plays a role in the exchange of products in the next stage after the barter operation is completed. The User Agents group is the group that contains the Customer agents. In this group, there are agents that request barter. In this study, the system was implemented using the JADE language [18]. In [71], an E-barter system was designed using MAS-CommonKADS methodology [72]. The focus of this system is in the negotiation phase. A bargaining protocol between two matched agents was presented. In [27], an E-barter system was designed and developed by using Prometheus methodology [44]. In this system, BDI agents are used which is not the case in the previous studies in the literature. System development is realized using JACK intelligent agent platform [20; 38]. Ontologies have been used for the matching purpose. There are two basic agents in the system. The Matchmaker agent is responsible for performing appropriate matching between customers. It benefits from ontologies for these mappings. The customer agents request bartering and negotiate with each other to realize the barter transaction. This study proposes a new rationale for the negotiations which takes the ratio of offer and need into consideration. We believe that our work on the development of E-barter systems contributes to the abovementioned efforts by first utilizing the semantic web services and capabilities of BDI agents for E-bartering instead of reactive agents which is preferred in most of the previous work. In addition, the remaining studies (e.g., [27]) which utilizing BDI model for E-bartering do not benefit from internal reasoning mechanism of BDI agents 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 of the other studies are generic for agent development, our study uses SEA_ML and its tool inside a domain-specific methodology. This makes the design and development of the MAS system easier and less-costly as the developer works with the domain concepts and benefits from generative capability of the provided tool. ## 6 Discussion and Conclusions

In this paper, a development methodology is proposed for development of MASs working in semantic web environments. This methodology is based on a DSML, called SEA_ML. The study is demonstrated using a case study for E-barter. To this end, the BDI agents and the SWSs for the E-barter system are analyzed, designed and developed using different viewpoints and features of SEA_ML. Also, a demonstration scenario is provided for the implemented system. In the traditional E-barter systems [62; 63; 64], customers and their products' information are stored in databases in a monolithic way. This approach has two major disadvantages. First, the system is not scalable. By adding different product categories, the maintenance effort and cost of the system will be increased. The second disadvantage is that a semantic approach cannot be achieved with the traditional methods. In this study, these two disadvantages have been overcome by using SWS. The use of SWS primarily ensures that the customer and the product information are stored categorically in the external services. This leads to a more scalable system. In addition, these services with the semantic structure allow a semantic logic to be implemented in the matching and gives the opportunity to the customers to communicate only with the appropriate services. This increases the likelihood that the barter process has successful result in a limited time.