Agent architectures have to cope with a number of internal properties (concerns), such as autonomy, learning, and mobility. As the agent complexity increases, these agent properties crosscut each other and the agent’s basic functionality. In addition, multi-agent systems encompass multiple agent types with heterogeneous architectures. Each of these agent types has different properties, which need to be composed in different ways. In this context, the separation and the flexible composition of agent concerns are crucial for the construction of heterogeneous agent architectures. Moreover the separation of agent concerns needs to be guaranteed throughout the different development phases, especially from the architectural to the implementation phase. Existing approaches do not provide appropriate support for the modularization of agent properties at the architectural stage, and do not promote a smooth transition to the system implementation. This paper presents an aspect-oriented method ...
Alessandro F. Garcia, Uirá Kulesza, Carlos