M.Sc. / This thesis investigates how intelligent agents can be used to solve airline scheduling problems. It is divided into three parts. The first states what airline scheduling consists of; the second discusses the results of a literature study; and the third consists of solutions to the problem. Airline scheduling consists of three major activities viz. market-driven flight generation, crew assignment and operational problem management. The market schedulers first create a flight set based on a forecast of passenger numbers and passenger preferences. The crew schedulers attempt to crew the flights generated by the market schedulers (subject to safety and rest regulations). The operational schedulers maintain the flights from seven days prior to the day of operation to one day after the end of the flight. Finding a global solution to this three-phase operation is the airline scheduling problem. An agent-based solution to the airline scheduling problem was the focus of this thesis. Agents encapsulate many useful artificial intelligence solution strategies. For the proposed solution to the market driven scheduling problem a distributed negotiation scheme using agents was used. A routing and an assignment agent were defined to assist the crew scheduler. Finally an operational scheduling agent was defined to solve the operational scheduling problem. The routing and assignment agents make use of FIFOqueues and genetic algorithms. The operational scheduling agent makes use of a traditional expert system combined with a learning algorithm to give it more flexibility. A prototype, developed in Java, was used to demonstrate how agents could solve the market driven scheduling problem. This distributed negotiation scheme was implemented on Sun SPARC workstations running the Solaris operating system. A prototype developed in Delphi was also developed to show how learning algorithms could be applied to the scheduling environment.

M.Ing. (Electrical And Electronic Engineering) / Bioinformatics is one of the most rapidly advancing sciences today. It is a scienti c domain that attempts to apply modern computing and information technologies to the eld of biology, the study of life itself and involves documenting and analysing genetics, proteins, viruses, bacteria and cancer as well as hereditary traits and diseases, as well as researching cures and treatments for whole ranges of health threats. The growth of bioinformatics and developments, both theoretical and experimental in biology, can largely be linked to the IT explosion which gives the eld more powerful processing options with much cheaper solutions, limited only by the steady yet signi cant improvements as promised by Moore's Law [3]. This IT explosion has also caused signi cant advances due to the high consumer demand region of computer graphics hardware, or GPUs (Graphics Processing Units). The consumer demand has actually managed to advance GPUs far faster than classical CPUs (Central Processing Units), outpacing CPU performance improvements by a large margin. As of early 2010, the fastest available PC processor(Intel Core i7 980 XE) has a theoretical performance of 107.55 GFLOPS [4], while GPUs with TFLOPS (1000 GFLOPS) of performance have been commercially available since 2008 (ATI HD4800). While typically used only for graphical rendering, modern innovations have greatly increased GPU exibility and has given rise to the eld of GPGPU (General Purpose GPU) which allows graphics processors to be applied to non-graphics applications. By utilizing GPU processing power to solve bioinformatics problems, the eld can theoretically be boosted once again, increasing the amount of computational power available to scientists by an order of magnitude or more...

M.Sc. (Computer Science) / Artificial Intelligence has been an increasing focus of study over the past years. Agent technology has emerged as being the preferred model for simulating intelligence [Jen00a]. Focus is now turning to inter-agent communication [Jen00b] and agents that can adapt to changes in their environment. Digital music has been gaining in popularity over the past few years. Devices such as Apple’s iPod have sold millions. These devices have the capability of holding thousands of songs. Managing such a device and selecting a list of songs to play from so many can be a difficult task. This dissertation expands on agent types by creating a new agent type known as the Modifiable Agent. The Modifiable Agent type defines agents which have the ability to modify their intelligence depending on what data they need to analyse. This allows an agent to, for example, change from being a goal based to a learning based agent, or allows an agent to modify the way in which it processes data. Digital music is a growing field with devices such as the Apple iPod revolutionising the industry. These devices can store large amounts of songs and as such, make it very difficult to navigate as they usually don’t include devices such as a mouse or keyboard. Therefore, creating a play list of songs can be a tiresome process which can lead to the user playing the same songs over and over. The goal of the dissertation is to provide research into methods of automatically creating a play list from a user selected song, i.e. once a user selects a song, a list of similar music is automatically generated and added to the user’s playlist. This simplifies the task of selecting music and adds diversity to the songs which the user listens to. The dissertation introduces intelligent music selection, or selecting a play list of songs depending on music classification techniques and past human interaction.

Researchers in artificial intelligence and agent technologies are presented with a massive array of various technologies that they might use for their research projects. It is difficult for researchers to test their theories effectively in the field. It takes a great deal of time to develop the platform on which the newly created agent will be tested, with little or no time left for troubleshooting and the investigation of further solutions. Every time a new technique or agent is researched, the agent has to be redeveloped from the ground up. This makes it difficult for researchers to compare their own theories with previously developed components. With the wide range of technologies and techniques available, there is no easy way to effectively make use of the various components, as each tool uses different technologies that cannot be combined easily. This dissertation outlines the new plug-in oriented agent architecture (POAA) and describes the agents that use the POAA. POAA agents make extensive use of functional and controller-based plug-ins in order to extend the functionality and behaviour of the agent. The architecture was designed to facilitate machine learning and agent mobility techniques. POAA agents are created by mounting newly created dynamic plug-in components into the static structure of the agent. The static structure of the agent serves as the basis of agent functionality and as the controller for the agent’s life cycle. The static and dynamic components of the POAA agent interact with each other in order to perform the agent’s required tasks. The use of plug-ins will greatly improve the effectiveness of researchers, as only a single, standard architecture will exist. Researchers only need design and develop the plug-in required for their specific agent to function as desired. This will also facilitate the comparison of various tools and methods, as only the components being reviewed need to be interchanged to measure system performance. The use of different plug-in architectures is also investigated. This includes deciding if the plug-in base will be configured at application run-time or at the time of application compilation. This dissertation focuses on techniques that will facilitate machine learning and agent mobility. For these purposes, extensive use is made of the machine learning tool WEKA developed by University of Waikato in New Zealand [Wi00]. The use of Java in the prototype will also facilitate the cross platform capability of the proposed agents. / Prof. E.M. Ehlers

M.Sc. (Computer Science) / The purpose of the research presented in this dissertation is to explore the use of a distributed multi-agent system in a general purpose crowd simulation model. Crowd simulation is becoming an increasingly important tool for analysing new construction projects, as it enables safety and performance evaluations to be performed on architectural plans before the buildings have been constructed. Crowd simulation is a challenging problem, as it requires the simulation of complex interactions of people within a crowd. The dissertation investigates existing models of crowd simulation and identifies three primary sub-tasks of crowd simulation: deliberation, path planning and collision-avoiding movement. Deliberation is the process of determining which goal an agent will attempt to satisfy next. Path planning is the process of finding a collision-free path from an agent‟s current location towards its goal. Collision-avoiding movement deals with moving an agent along its calculated path while avoiding collisions with other agents. A multi-agent crowd simulation model, DiMACS, is proposed as a means of addressing the problem of crowd simulation. Multi-agent technology provides an effective solution for representing individuals within a crowd; each member of a crowd can be represented as an intelligent agent. Intelligent agents are capable of maintaining their own internal state and deciding on a course of action based on that internal state. DiMACS is capable of producing realistic simulations while making use of distributed and parallel processing to improve its performance. In addition, the model is highly customisable. The dissertation also presents a user-friendly method for configuring agents within a simulation that abstracts the complexity of agent behaviour away from a user so as to increase the accessibility of configuring the proposed model. In addition, an application programming interface is provided that enables developers to extend the model to simulate additional agent behaviours. The research shows how distributed and parallel processing may be used to improve the performance of an agent-based crowd simulation without compromising the accuracy of the simulation.

M.Sc. (Computer Science) / Artificial intelligence strives towards providing an autonomous mechanism by which the environments of humans may be affected beneficially. The steps taken towards this goal have been to create individual computer programs that solve small problems; however, larger world problems need to be addressed. Intelligence in computer systems cannot be seen as a single algorithm which solves all problems, but rather a set of distinctive algorithms which may be combined uniquely in order to achieve a particular goal. One field of application for artificial intelligence in service to humanity is robotics. Autonomous robotic entities are becoming more commonplace in society, making their behaviour an important topic of study. Machines capable of performing various activities in service of the human race are fundamentally important if they are to be trusted to perform activities which could affect the health or well-being of their creators. The aim of the following research is to present the autonomous two-level agent framework (ATAF), a framework for intelligent agents to operate within a robotic entity. The entity must be able to adapt to various environments and situations and react in a manner consistent with its environment.

No description available.

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.