## 4 Conclusion

Table 1: Details of the Customer Agents in the E-barter System. In addition to abovementioned AOSE methodologies, the researchers have significant efforts on using model-driven approaches for agent development and the derivation of DSMLs for MAS. For instance, Agent-DSL [56] is used to specify the agency properties that an agent needs to accomplish its tasks. However, the proposed language is presented only with its metamodel and provided just a visual modeling of the agent systems according to agent features, such as knowledge, interaction, adaptation, autonomy and collaboration. The A&A metamodel introduced in [8] considers the notion of artifacts for agents. In the A&A metamodel, agents are modeled as proactive entities for the systems' goals and tasks while the artifacts represent the reactive entities providing the services and the functions and, hence, constitute the environment for MAS. The FAML metamodel, introduced in [9], is a synthesis of various existing metamodels for agent systems. Design time and runtime concepts for MASs are given and validation of these concepts is provided by their use at various MAS development methodologies. [PERSON] [11] introduces a DSML for MAS called DSML4 MAS. The abstract syntax of the DSML is derived from a platform independent metamodel structured into several aspects, each focusing on a specific viewpoint of a MAS. To provide a concrete syntax, the appropriate graphical notations for the concepts and relations are defined. Furthermore, DSML4 MAS supports the deployment of modeled MASs both in JACK and JADE agent platforms by providing an operational semantics over model transformations [57]. DSML4 MAS also guides MDD of different agent applications. For instance, [PERSON] et al. [58] use DSML4 MAS for the development of agent-based ambient intelligence systems. The metamodel of DSML4 MAS is employed as a source metamodel to support the modeling of context aware systems and conforming models are transformed into target models which are instances of an aspect-oriented agent metamodel called Malaca. Code generation enables the implementation of Malaca models to run in the ambient intelligence devices. Another model driven MAS development approach is provided in [12] with introducing a new DSML. The abstract syntax of the DSML is presented using the Meta-object Facility (MOF), the concrete syntax and its tool is provided with Eclipse Graphical Modeling Framework (GMF), and finally the code generation for the JACK agent platform is realized with model transformations using Eclipse JET. The language supports modeling of agents according to Prometheus methodology [45]. A similar study is performed in [59] which proposes a technique for the definition of agent-oriented engineering process models and can be used to define processes for creating both hardware and software agents. This study also offers a related MDD tool based on INGENIAS methodology [52]. The work conducted in [13] aims at creating a UML-based agent modeling language, called MAS-ML, which can model the well-known types of agent internal architectures, namely simple reflex agent, model-based agent, reflex agent, goal-based agent and utility-based agent. Representation and exemplification of all supported agent architectures in the concrete syntax of the introduced language are given. MAS-ML is also accompanied with a graphical tool which enables agent modeling. However, the current version of MAS-ML does not support any code generation for MAS frameworks which prevents the execution of the modeled agent systems. [PERSON] and [PERSON] [60] investigate how a model-driven framework can be constructed to develop agent-oriented software by proposing strategic, tactical and operational views. Within this context, they introduced a Strategic Services Model in which strategic agent services can be modeled and then transformed into the dependencies modeled according to the well-known i* early phase system modeling language [61] for a problem domain.