## 2 SEA_ML-Based MAS Development Methodology  
In this study, a model driven approach is adopted, and a model-based methodology is proposed for design and implementation of semantic web-enabled MASs. To this end, the proposed methodology covers the use of a DSML. In this way, the complex systems including SWSs and MAS components are modeled at a higher level of abstraction. In addition, these languages can model the interaction between SWSs and Agents. As a result, the system can be analyzed, and the required elements can be designed using the terms and notations very close to the domain. These domain-specific elements and their relations to each other creates the domain-specific instance models which pave the way to implement the system. As these models are persisted in a structural and formal way,they can be transformed to other proper paradigms, such as mathematical logics. In this way, they can be formally analyzed and validated based on formal methods. This can decrease the number of semantic errors later in the developed system. Furthermore, these models can be used to automatically generate the architectural codes for agents and artifacts of the complex systems which can end up with less syntactical errors and speed up the development procedure. According to the definition of artifacts in Agents & Artifacts (A&A) metamodel [8], artifacts in our study are environmental components and entities providing services, such as OWL-S documents (including process, grounding, and interface documents) for SWS. Faster development requires less efforts and it brings cost reduction in the projects. Moreover, fewer syntactical and semantical errors mean less iterations in the development phase and less testing phase which also reduce the cost and effort. Therefore, the system can be checked, and the errors can be partially found in the early phases of development, namely analysis and design phases, instead of finding them in the implementation and testing phases. In the scope of this study, SEA_ML[17] is used as a DSML for the construction of semantic web-enabled MASs. SEA_ML enables the developers to model the agent systems in a platform independent level and then 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 also 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 specifically pave the way for the development of software agents working on the Semantic Web environment. Modeling agents, agent knowledge-bases, platform ontologies, SWS and interactions between agents and SWS are all possible in SEA_ML. SEA_ML's metamodel is divided into eight viewpoints, each of which represents a different aspect for developing Semantic Web-enabled MASs. Agent's Internal Viewpoint is related to the internal structures of semantic web agents (SWAs) and defines entities and their relations required for the construction of agents. Interaction Viewpoint expresses the interactions and the communications in a MAS by taking messages and message sequences into account. MAS Viewpoint solely deals with the construction of a MAS as a whole. It includes the main blocks which compose the complex system as an organization. Role Viewpoint delves into the complex controlling structure of the agents and addresses role types. Environmental Viewpoint describes the use of resources and interaction between agents with their surroundings. Plan Viewpoint deals with an agent Plan's internal structure, which is composed of Tasks and atomic elements such as Actions.