Abstract
M.Sc.(Computer Science)
Agent technology has become more common in mainstream applications as it allows systems to perform routine operations without input from human users. The current evolution of the internet and the increasingly distributed nature of commercial interests, such as bidding auctions, personal shopping assistants and corporate management systems, require software in a distributed environment to be capable of acting autonomously. Multi-agent systems have emerged to deal with these distributed environments which can range in size from a couple of agents to potentially infinite agents [Vla03, Rus03].
When considering a cooperative multi-agent system, it is important for agents to coordinate effectively. Strategic game theory has introduced a means to coordinate by providing social conventions and roles [Vla03]. Both social conventions and roles help simplify the coordination problem between agents when performing coordination actions. An additional simplification of the coordination problem is to utilise coordination graphs. This reduces the number of agents in the environment to consider for a coordination action [Gue02].
Communication in multi-agent systems extends the ability of agents to coordinate with one another. It allows the removal of the requirement to determine the state of participating agents by inspection. Instead, an agent could request the state of another agent by utilising a communication action. Communication does require an additional level of management since agents are rarely allowed to communicate freely and the communication language is not always guaranteed to be standard between agents [Cha02, Vla03].
The dissertation covers the background information regarding multi-agent systems and focuses on the elements that are unique to these systems, such as coordination, communication and methods to represent knowledge structures in a multi-agent system. A model is then proposed as a framework in which scalable populations of agents are able to coordinate when limited knowledge is available about other agents in the environment. The model, which is called the Controller Operator Graph (COG) model, introduces two unique agent types which help coordinate a large population of agents. The unique agents are provided to assist with communication and coordination in the COG model.
The COG model is designed to help agents coordinate in a dynamic environment by providing mechanisms to monitor agent population and goal states. The operator agent is responsible for maintaining communication links between agents and provides the ability to monitor a population of agents for the multi-agent system. The controller agent is responsible for ensuring that coordination actions are performed between agents which have no prior knowledge of one another. It provides a means to handle a dynamic situation in which the coordination actions can be extended beyond the original requirements.
An implementation of the COG model is provided utilising a supply chain scenario which compares increasing agent populations. The COG implementation demonstrates by means of unified modelling language diagrams a method to design and develop the different concepts in the COG model, such as the execution tree, controller agent and operator agent. The implementation demonstrates the strengths of the COG model, which are handling dynamic environments and achieving dynamic goal states for the environment. The implementation also indicates some of the weaknesses in the COG model, such as greedy agent selection by the controller agent, and single points of failure.