## 4 Conclusion
In this study, we also experienced that the proactive behavior of the BDI agents may help the fruitful application of E-barter systems by especially preventing bartering the goods ineffectively with undesired exchange ratios. Implemented BDI agents in here aim at choosing the most appropriate plan to achieve the maximum gain out of the bargaining on behalf of their users. It is possible to develop a similar MAS for the same purpose with agent models other than BDI which probably leads to provide desired efficiency in bargaining. However, in addition to the achieved fruitfulness, we also found modeling and implementation of the MAS for e-bartering convenient by utilizing BDI constructs and their relations. On the other hand, using the proposed methodology, an efficient implementation of the system is possible through an accurate design with few errors in the analysis and the design phases [21]. In our work, semantic errors can be detected during the analysis and design phase using a SEA_ML-based methodology and the implementation process is completed in a shorter time with fewer problems. Achieved results validated the generation and the development-time performance of SEA_ML discussed in [21]. For the E-barter case study discussed in this paper, application of the SEA_ML-based MAS development methodology enabled the generation of approximately more than half of the whole agents and artifacts. Despite the abovementioned advantages, the proposed approach in this work can be improved in several ways. Our future work consists of the followings: The semantic web services in the system are currently added to the system in a static way to facilitate the development These services can be expected to be added to the system automatically by communicating with the E-barter system. Moreover, the negotiation logic between the customers is presented at a basic level. Successful barter transactions may require a more complex negotiation rationale [73]. In addition, the business logic in the agent plans are mostly developed as the delta codes and hence our aim is to leverage the comprehensiveness of the models. In this way, it is possible to reduce the delta code and gain more generation performance using the proposed methodology. We also plan to empower the current methodology by using the formal methods discussed in [74; 75] for SEA_ML to validate the models and check some domain properties in the design level for the MAS. As another future study, our aim is to provide the execution of SEA_ML agents on Jason platform [47]. Similar to operational semantics of SEA_ML currently provided for JACK, MAS models prepared in SEA_ML can be transformed into Jason model instances and code generation can also be possible for this platform. Recently, we derived a metamodel [76] for Jason agents and we plan to use this metamodel as another PSMM and prepare a series of M2M transformations between SEA_ML metamodel and this metamodel which will lead to generation of Jason agents inside the MAS development methodology proposed in this paper. [PERSON] and [PERSON] conceived the proposed MAS development methodology. [PERSON] also leaded the writing of the paper. [PERSON] and [PERSON] implemented the E-barter system and performed the experiments. [PERSON] contributed to the analysis and assessment of the results. This work is partially funded by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant 115E591. The authors declare no conflict of interest. ## References

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