An agent-based service-oriented approach to evolving legacy software systems into a pervasive computing environment.

This thesis focuses on an Agent-Based Service-Oriented approach to evolving legacy system into a Pervasive Computing environment. The methodology consists of multiple phases: using reverse engineering techniques to comprehend and decompose legacy systems, employing XML and Web Services to transform and represent a legacy system as pervasive services, and integrating these pervasive services into pervasive computing environments with agent based integration technology. A legacy intelligent building system is used as a case study for experiments with the approach, which demonstrates that the proposed approach has the ability to evolve legacy systems into pervasive service environments seamlessly. Conclusion is drawn based on analysis and further research directions are also discussed

An agent-based service oriented architecture for risk mining

University of Technology, Sydney. Faculty of Engineering and Information Technology.Risk Mining (RM) is the process of analyzing data including risk information by data mining methods, with the mining results for risk prevention. In the last few years, some researchers have proposed the combination of data mining and agent technology (agent mining) to improve the performance of data mining methodology in the heterogeneous business environments. However, problems exist for further research with the application of risk mining systems in real industry environments to enhance the robustness of system architect, dynamic business process and model accuracy etc. Therefore, in this thesis we present an Agent-based Service-oriented Risk Mining Architecture (ABSORM), which has been designed to facilitate the development of agent mining systems to address the above issues. This thesis focuses on developing the following strategies: • The integration of agent technology with web service. In this framework, we propose a new and easier method, by which the system functions are not integrated into the structure of the agents, rather modeled as distributed services and applications which are invoked by the agents acting as controllers and coordinators. Therefore, techniques developed in this framework can improve the interoperability between different modules, distribution of resources, and the lack of dependency of programming languages. • The integration of agent technology with business process management. In this work, we develop the autonomous agents that can collaborate in a business flow, which not only increases the reusability of the system, but also eases the system development in terms of re-usability of the computational resources. A group of agents solves problems in the following way: each individual agent solves the problem individually, and then interacts with each other to finalize a business process. • The integration of agent technology with ensemble learning methods. In this thesis, we are interested in developing agent-based ensemble learning strategies for risk mining: each ensemble agent individually gathers the evidence about model evaluation, and then ensembles learning methods like bagging and boosting is used to obtain prediction from the individually gathered evidence. Agent based ensemble learning can provide a critical boost to risk mining where predictive accuracy is more vital than model interpretability. The proposed architecture has been evaluated for building an online banking fraud detection system and a student risk management system. These two applications have been proved to be a sophisticated, yet user friendly, risk analysis and management tool. They are modular, interactive, dynamic and globally oriented

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

A Semantic Grid Oriented to E-Tourism

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.Comment: 12 PAGES, 7 Figure