Modeling Organizational Structures and Agent Knowledge for Mulan Applications

In software engineering the initial setup of a system, as well as the self-adaptation processes within a system are challenging tasks. We address these challenges by providing explications of organizational structures through modeling. In this paper we present a service-oriented perspective on the organizational structure of MAS and we present modeling techniques and tools for supporting this perspective. We pursue a model-driven approach and a tight integration between various models on the one hand and between the models and the generated code on the other hand. In particular, we use ontologies to define a meta-model for organizational structures in a way that we can easily generate the initial content of agent knowledge bases in the form of FIPA semantic language (SL) fragments, depending on the positions the agents occupy in the context of the organizational structure. This allows the agents to reason and to communicate about their organizational embedding, which is a prerequisite for self-adaptation in multi-agent systems.