Abstract
Abstract As demands for behaviorally sophisticated software grow, agent-based systems are increasingly being employed. Software agents are frequently applied to large, complex systems that involve interdisciplinary development teams. These complex systems have proved to be challenging to develop and evolve, even for the most competent software engineers. Taking lessons learned in other engineering disciplines such as computer and architectural engineering we investigated a model-based engineering approach called Model-Driven Architecture (MDA) to automate, whenever possible, the development and evolution of agent-based applications. In our investigation, we use the Cognitive Agent Architecture (Cougaar); one of the most mature and sophisticated collaborative agent-based architectures. MDA and Cougaar served as the primary components and implementation platform for our research. In this paper we present our approach and demonstrate how MDA is effective for producing sophisticated agent-based systems. A key challenge was found in designing a flexible meta-model framework that would accommodate both top-down domain information and bottom-up platform specific constructs, as well as the transformations and mappings between them. We employed a General Domain Application Model (GDAM) as the platform-independent model layer and General Cougaar Application Model (GCAM) layer as the platform specific model respectively. Domain-level requirements are formulated using a XML Process Definition Language (XPDL) based graphical editor and are the refined through a series of model transformations (via the underlying metamodel) to systematically generate the agent-based software system. Through an illustrative case-study, we report on the feasibility, strengths and limitations of the model-based approach as it was investigated with the Cougaar.
Highlights
As society increasingly depends on software, the size and complexity of software systems continues to grow making them progressively more difficult to understand and evolve
They are task-oriented and often adaptive, and may involve autonomic behaviors, or engage in collaborative or competitive activities. These and other aspects make it challenging to develop and evolve agent-based systems in a timely fashion. To address this key challenge, we investigate the Object Management Group’s (OMG) Model Driven Architecture (MDA) approach [6, 17, 24], which aims at separating application logic from the underlying technologies to improve reusability, portability and development processes
We investigate how Cougaar components may be composed into a General Cougaar Application Model (GCAM) and used to develop a General Domain Application Model (GDAM) for specifying and generating software applications
Summary
As society increasingly depends on software, the size and complexity of software systems continues to grow making them progressively more difficult to understand and evolve. These agent-based applications are sophisticated, complex, and very hard to develop Interdisciplinary development of these systems has emerged as a way to ensure that relevant requirements are rendered properly as the abstract models from the problem domain evolve into increasingly more detailed and complete ones used to generate software. Software architecture provides a framework to understand dependencies that exist between the various components, connections, and configurations reflected in the requirements These emergent technologies provide a reasonable basis for addressing complexity issues by separating concerns (integration, interoperability, decision support, and the like) and allowing agents to provide the necessary processing. The core representation includes: Agents, Communities, Societies, Plugins, Assets, Preferences, Knowledge Rules, Policies, Rules, Constraints, Events, Facts, Services, Service Providers, Tasks, Nodes, Subscriptions, Predicate, Messages, Directives, Logic Providers, Hosts, Domains, and Configuration Beneath these are are the Java and lowerlevel constructs relevant to the implementation platform. We chose to leave this area to our research partners as part of their scope and it is not reported in this research
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