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M.Sc. (Information Technology) / Data is generated at an ever-increasing rate and it has become difficult to process or analyse it in its raw form. The most data is generated by processes or measuring equipment, resulting in very large volumes of data per time unit. Companies and corporations rely on their Management and Information Systems (MIS) teams to perform Extract, Transform and Load (ETL) operations to data warehouses on a daily basis in order to provide them with reports. Data mining is a Business Intelligence (BI) tool and can be defined as the process of discovering hidden information from existing data repositories. The successful operation of data mining algorithms requires data to be pre-processed for algorithms to derive IF-THEN rules. This dissertation presents a data pre-processing model to transform data in an intelligent manner to enhance its suitability for data mining operations. The Extract Pre- Process and Save for Data Mining (EPS4DM) model is proposed. This model will perform the pre-processing tasks required on a chosen dataset and transform the dataset into the formats required. This can be accessed by data mining algorithms from a data mining mart when needed. The proof of concept prototype features agent-based Computational Intelligence (CI) based algorithms, which allow the pre-processing tasks of classification and clustering as means of dimensionality reduction to be performed. The task of clustering requires the denormalisation of relational structures and is automated using a feature vector approach. A Particle Swarm Optimisation (PSO) algorithm is run on the patterns to find cluster centres based on Euclidean distances. The task of classification requires a feature vector as input and makes use of a Genetic Algorithm (GA) to produce a transformation matrix to reduce the number of significant features in the dataset. The results of both the classification and clustering processes are stored in the data mart.

Agents that act as user assistants will become invaluable as the number of information sources continue to proliferate. Such agents can support the work of users by learning to automate time-consuming tasks and filter information to manageable levels. Although considerable advances have been made in this area, it remains a fertile area for further development. One application of agents under careful scrutiny is the automated negotiation of conflicts between different user's needs and desires. Many techniques require explicit user models in order to function. This dissertation explores a technique for dynamically constructing user models and the impact of using them to anticipate the need for negotiation. Negotiation is reduced by including an advising aspect to the agent that can use this anticipation of conflict to adjust user behavior.

This thesis describes experiments designed to measure the effect of collaborative communication on task performance of a multiagent system. A discrete event simulation was developed to model a multi-agent system completing a task to find and collect food resources, with the ability to substitute various communication and coordination methods. Experiments were conducted to find the effects of the various communication methods on completion of the task to find and harvest the food resources. Results show that communication decreases the time required to complete the task. However, all communication methods do not fare equally well. In particular, results indicate that the communication model of the bee is a particularly effective method of agent communication and collaboration. Furthermore, results indicate that direct communication with additional information content provides better completion results. Cost-benefit models show some conflicting information, indicating that the increased performance may not offset the additional cost of achieving that performance.

The Internet supports a perpetually evolving patchwork of network services and applications. Popular applications include the World Wide Web, online commerce, online banking, email, instant messaging, multimedia streaming, and online video games. Practically all networked applications have a common objective: to directly or indirectly process requests generated by humans. Some users employ automation to establish an unfair advantage over non-automated users. The perceived and substantive damages that automated, adversarial users inflict on an application degrade its enjoyment and usability by legitimate users, and result in reputation and revenue loss for the application's service provider. This dissertation examines three challenges critical to addressing the undesirable automation of networked applications. The first challenge explores individual methods that detect various automated behaviors. Detection methods range from observing unusual network-level request traffic to sensing anomalous client operation at the application-level. Since many detection methods are not individually conclusive, the second challenge investigates how to combine detection methods to accurately identify automated adversaries. The third challenge considers how to leverage the available knowledge to disincentivize adversary automation by nullifying their advantage over legitimate users. The thesis of this dissertation is that: there exist methods to detect automated behaviors with which an application's service provider can identify and then systematically disincentivize automated adversaries. This dissertation evaluates this thesis using research performed on two network applications that have different access to the client software: Web-based services and multiplayer online games.

The present study has originated from the realisation that numerous software development projects either do not live up to expectations or fail outright. The scope, environment and implementation of traditional software projects have changed due to various reasons such as globalisation, advances in computing technologies and, last but not least, the development and deployment of software projects in distributed, collaborative and virtual environments. As a result, traditional project management methods cannot and do not address the added complexities found in this ever-changing environment. In this study the processes and procedures associated with software project management (SPM) were explored. SPM can be defined as the process of planning, organising, staffing, monitoring, controlling and leading a software project. The current study is principally aimed at making a contribution to enhancing and supporting SPM. A thorough investigation into software agent computing resulted in the realisation that software agent technology can be regarded as a new paradigm that may be used to support the SPM processes. A software agent is an autonomous system that forms part of an environment, can sense the environment and act on it over a period of time, in pursuit of its own agenda. The software agent can also perceive, reason and act by selecting and executing an appropriate action. The unique requirements of SPM and the ways in which agent technology may address these were subsequently identified. It was concluded that agent technology is specifically suited to geographically distributed systems, large network systems and mobile devices. Agents provide a natural metaphor for support in a team environment where cooperation and the coordination of actions toward a common goal, as well as the monitoring and controlling of actions are strongly supported. Although it became evident that agent technology is indeed being applied to areas and sections of the SPM environment, it is not being applied to the whole spectrum, i.e. to all core and facilitating functions of SPM. If software agents were to be used across the whole spectrum of SPM processes, this could provide a significant advantage to software project managers who are currently using other contemporary methods. The "SPMSA" model (Software Project Management supported by Software Agents) was therefore proposed. This model aims to enhance SPM by taking into account the unique nature and changing environment of software projects. The SPMSA model is unique as it supports the entire spectrum of SPM functionality, thus supporting and enhancing each key function with a team of software agents. Both the project manager and individual team members will be supported during software project management processes to simplify their tasks, eliminate the complexities, automate actions and enhance coordination and communication. Virtual teamwork, knowledge management, automated workflow management and process and task coordination will also be supported. A prototype of a section of the risk management key function of the SPMSA model was implemented as `proof of concept'. This prototype may be expanded to include the entire SPMSA model and cover all areas of SPM. Finally, the SPMSA model was verified by comparing the SPM phases of the model to the Plan-Do-Check-Act (PDCA) cycle. These phases of the SPMSA model were furthermore compared to the basic phases of software development as prescribed by the ISO 10006:2003 standard for projects. In both cases the SPMSA model compared favourably. Hence it can be concluded that the SPMSA model makes a fresh contribution to the enhancement of SPM by utilising software agent technology. / School of Computing / Ph. D. (Computer Science)

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Peer-to-Peer (P2P) networks have seen tremendous growth in development and usage in recent times. This attention has brought many developments as well as new challenges to these networks. We will show that agent extensions to P2P networks offer solutions to many problems faced by P2P networks. In this research, an attempt is made to bring together JXTA P2P infrastructure and Jinni, a Prolog based agent engine to form an agent based P2P network. On top of the JXTA, we define simple Java API providing P2P services for agent programming constructs. Jinni is deployed on this JXTA network using an automated code update mechanism. Experiments are conducted on this Jinni/JXTA platform to implement a simple agent communication and data exchange protocol.

D.Phil. / A modern day N-P complete problem is the assigning of frequencies to transmitters in a cellular network in such a manner that, ideally, no two transmitters in the same cell or neighbouring cells use the same frequency. Considering that an average cellular network provider has over 29 000 transmitters and only 55 frequencies, choosing these frequencies in an optimal way is a very difficult computational problem. Swarm intelligence allows the acceptable minimization and optimization of the frequency assignment problem (FAP). Swarm intelligence is a concept modelling the processes in natural systems such as ant colonies, beehives, human immune systems and the human brain. These systems are selforganizational and display high efficiency in the execution of their tasks. A number of simple automated agents interacting with each other and the environment form a collective. Specifically, there is no "central agent" directing the others. A collective can display surprising intelligence which emerges out of the interaction of the individual agents. This collective intelligence, referred to as swarm intelligence, is displayed in ant colonies when ants build elaborate nests, regulate nest temperature and efficiently search for food in very complex environments. In this thesis a proposal is made to utilize swarm intelligence to build a swarm automatic frequency planner (swarm AFP). The swarm AFP produces frequency plans that are better, or on par with existing frequency planning tools, and in a fraction of the time. A swarm AFP is presented through an in-depth investigation into complex adaptive systems, agent architectures and emergence. Based on an understanding of these concepts, a swarm intelligence model called ACEUS is constructed. ACEUS forms the platform of the swarm AFP. It is a contribution to multi-agent technology as it is a new multi-agent framework that exhibits swarm intelligence and complex distributed computation. What differentiates ACEUS from other multi-agent technologies is that ACEUS works on the basis that the tasks or constructions that have been created by the agents actually guide the agents in their endeavours. There is no centralised agent controlling or guiding the process. The agents in ACEUS receive information and stimulation from their tasks or constructions in the environment. As these constructions or tasks alter the environment, the agents receive stimulus from the changing environment and then react to the changing environment. The changing environment acts as an emergent guiding force to the agents. This is the important contribution that stigmergy contributes to ACEUS. Utilizing this concept, ACEUS is used to create a swarm AFP. The swarm AFP is benchmarked against the COST 259 Siemens benchmarks. In all the COST 259 Siemens scenarios the swarm AFP produced the best results in the shortest time. The swarm AFP was also tested in a real cellular network and the resulting statistics before and after the swarm AFP implementation are presented.