Agent-based open connectivity for decision support systems

Zhang, Hao Lan (2007) Agent-based open connectivity for decision support systems. PhD thesis, Victoria University.


One of the major problems that discourages the development of Decision Support Systems (DSSs) is the un-standardised DSS environment. Computers that support modern business processes are no longer stand-alone systems, but have become tightly connected both with each other and their users. Therefore, having a standardised environment that allows different DSS applications to communicate and cooperate is crucial. The integration difficulty is the most crucial problem that affects the development of DSSs. Therefore, an open and standardised environment for integrating various DSSs is required. Despite the critical need for an open architecture in the DSS designs, the present DSS architectural designs are unable to provide a fundamental solution to enhance the flexibility, connectivity, compatibility, and intelligence of a DSS. The emergence of intelligent agent technology fulfils the requirements of developing innovative and efficient DSS applications as intelligent agents offer various advantages, such as mobility, flexibility, intelligence, etc., to tackle the major problems in existing DSSs. Although various agent-based DSS applications have been suggested, most of these applications are unable to balance manageability with flexibility. Moreover, most existing agent-based DSSs are based on agent-coordinated design mechanisms, and often overlook the living environment for agents. This could cause the difficulties in cooperating and upgrading agents because the agent-based coordination mechanisms have limited capabilities to provide agents with relatively comprehensive information about global system objectives. This thesis proposes a novel multi-agent-based architecture for DSS, called Agentbased Open Connectivity for Decision support systems (AOCD). The AOCD architecture adopts a hybrid agent network topology that makes use of a unique feature called the Matrix-agent connection. The novel component, i.e. Matrix, provides a living environment for agents; it allows agents to upgrade themselves through interacting with the Matrix. This architecture is able to overcome the difficulties in concurrency control and synchronous communication that plague many decentralised systems. Performance analysis has been carried out on this framework and we find that it is able to provide a high degree of flexibility and efficiency compared with other frameworks. The thesis explores the detailed design of the AOCD framework and the major components employed in this framework including the Matrix, agents, and the unified Matrices structure. The proposed framework is able to enhance the system reusability and maximize the system performance. By using a set of interoperable autonomous agents, more creative decision-making can be accomplished in comparison with a hard-coded programmed approach. In this research, we systematically classified the agent network topologies, and developed an experimental program to evaluate the system performance based on three different agent network topologies. The experimental results present the evidence that the hybrid topology is efficient in the AOCD framework design. Furthermore, a novel topological description language for agent networks (TDLA) has been introduced in this research work, which provides an efficient mechanism for agents to perceive the information about their interconnected network. A new Agent-Rank algorithm is introduced in the thesis in order to provide an efficient matching mechanism for agent cooperation. The computational results based on our recently developed program for agent matchmaking demonstrate the efficiency and effectiveness of the Agent-Rank algorithm in the agent-matching and re-matching processes

Item type Thesis (PhD thesis)
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences
Keywords decision support systems, integration, intelligent agents, connectivity
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