In this paper, we discuss a way to model Agent-Environment interactions in adaptive multi-agent systems. We describe the interactions at various levels of abstraction starting with the highest. For each level, we specify the interactions from four viewpoints functional, behavioral, dataand structural. This results into a set of models that capture the main properties of agent-environment interactions.

In this paper, we assess five tools that allow the specification and execution of multi-agent based simulations. These tools are NetLogo, MASON, Ascape, RePastS and DIVAs.

This paper presents the idea that agents and environment play an equally important role in MAS design and proposes an architecture for agent environment system (AES). This architecture is based on software engineering concepts such as separation of concerns, modularity and information hiding. This leads to adaptable systems that can support multiple application domains. These concepts are illustrated through DIVAs, a geographically based social simulation tool, which offers flexibility without regulating simulation developers to a specific problem domain. The purpose of this paper is to introduce the motivations and concepts surrounding the AES architecture and show how DIVAs realizes the concepts of AES for one application domain.

In this paper, we discuss the model of an environment for a geographically based simulation system. The environment is structured as a graph in which nodes represent locations and edges represent paths between locations. The space is decomposed into a network of cells which are managed by cell controllers. In order to visualize location information at various levels of abstraction, we define the environment as a cell hierarchy.

Software engineers have long needed a way to understand complex software systems during all phases of the lifecycle. This need is driven by the fact that, in Software Engineering, there is ample evidence that a clear and visual representation of a software product can significantly enhance its understandability and reduce the lifecycle cost. For instance, when large volumes of data or text are to be understood or analyzed, it is often the case that a simple visual representation of the information allows the user to quickly and accurately detect discrepancies caused by confusing software documents. This early understanding reduces the person-months needed to take the project through successful system implementation.